1

ProUCL Version 4.0 


Technical Guide 


R E S E A R C H A N D D E V E L O P M E N T

2 
i

EPA/600/R-07/041 

April 2007 

www.epa.gov 

ProUCL Version 4.0 


Technical Guide 


Prepared for 


Brian Schumacher 

U.S. Environmental Protection Agency 


Office of Research and Development 


National Exposure Research Laboratory 


Environmental Sciences Division 


Technology Support Center 


Characterization and Monitoring Branch 


944 E. Harmon Ave. 


Las Vegas, NV 89119 


Prepared by 

Anita Singh, Ph.D.

1 

Ashok K. Singh, Ph.D.

2 


1

Lockheed Martin Environmental Services 

1050 E. Flamingo Road, Suite N240 

Las Vegas, NV 89119 

2

Department of Hotel Management 

University of Nevada, Las Vegas 

Las Vegas, NV 89154 

Notice: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official 

Agency policy. Mention of trade names and commercial products does not constitute endorsement or 

recommendation for use. 

U.S. Environmental Protection Agency 

Office of Research and Development 


Washington, DC 20460
 129cmb07 
ii 
Notice 

The United States Environmental Protection Agency (EPA) through its Office of Research and 

Development funded and managed the research described here. It has been peer reviewed by the EPA and 

approved for publication. Mention of trade names or commercial products does not constitute 

endorsement or recommendation by the EPA for use. 

ProUCL software was developed by Lockheed Martin under a contract with the EPA and is made 

available through the EPA Technical Support Center in Las Vegas, Nevada. 

Use of any portion of ProUCL that does not comply with the ProUCL Technical Guide is not 

recommended. 

ProUCL contains embedded licensed software. Any modification of the ProUCL source code may violate 

the embedded licensed software agreements and is expressly forbidden. 

ProUCL software provided by the EPA was scanned with McAfee VirusScan v4.5.1 SP1 and is certified 

free of viruses. 

With respect to ProUCL distributed software and documentation, neither the EPA nor any of their 

employees, assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed. Furthermore, software and documentation are 

supplied “as-is” without guarantee or warranty, expressed or implied, including without limitation, any 

warranty of merchantability or fitness for a specific purpose. 

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Executive Summary 

Statistical inference, including both estimation and hypotheses testing approaches, is routinely used to: 

1. Estimate environmental parameters of interest, such as exposure point concentration 

(EPC) terms, not-to-exceed values, and background level threshold values (BTVs) for 

contaminants of potential concern (COPC), 

2. Identify areas of concern (AOC) at a contaminated site, 

3. Compare contaminant concentrations found at two or more AOCs of a contaminated site, 

4. Compare contaminant concentrations found at an AOC with background or reference 

area contaminant concentrations, and 

5. Compare site concentrations with a cleanup standard to verify the attainment of cleanup 

standards. 

Several exposure and risk management and cleanup decisions in support of United States Environmental 

Protection Agency (EPA) projects are often made based upon the mean concentrations of the COPCs. A 

95% upper confidence limit (UCL95) of the unknown population (e.g., an AOC) arithmetic mean (AM), 

μ1, can be used to: 

• Estimate the EPC term of the AOC under investigation, 

• Determine the attainment of cleanup standards, 

• Compare site mean concentrations with reference area mean concentrations, and 

• Estimate background level mean contaminant concentrations. The background mean 

contaminant concentration level may be used to compare the mean of an area of concern. 

It should be noted that it is not appropriate to compare individual point-by-point site 

observations with the background mean concentration level. 

It is important to compute a reliable and stable UCL95 of the population mean using the available data. 

The UCL95 should approximately provide the 95% coverage for the unknown population mean, μ1. Based 

upon the available background data, it is equally important to compute reliable and stable upper 

percentiles, upper prediction limits (UPLs), or upper tolerance limits (UTLs). These upper limits based 

upon background (or reference) data are used as estimates of BTVs, compliance limits (CL), or not-toexceed values. These upper limits are often used in site (point-by-point) versus background comparison 

evaluations. 

Environmental scientists often encounter trace level concentrations of COPCs when evaluating sample 

analytical results. Those low level analytical results cannot be measured accurately and, therefore, are 

typically reported as less than one or more detection limit (DL) values (also called nondetects). However, 

practitioners need to obtain reliable estimates of the population mean, μ1, and the population standard 

deviation, σ1, and upper limits including the UCL of the population mass or mean, the UPL, and the UTL 

based upon data sets with nondetect (ND) observations. Additionally, they may have to use hypotheses 

testing approaches to verify the attainment of cleanup standards, and compare site and background 

concentrations of COPCs as mentioned above. 

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Background evaluation studies, BTVs, and not-to-exceed values should be estimated based upon 

defensible background data sets. The estimated BTVs or not-to-exceed values are then used to identify the 

COPCs, to identify the site AOCs or hot spots, and to compare the contaminant concentrations at a site 

with background concentrations. The use of appropriate statistical methods and limits for site versus 

background comparisons is based upon the following factors: 

1. Objective of the study, 

2. Environmental medium (e.g., soil, groundwater, sediment, air) of concern, 

3. Quantity and quality of the available data, 

4. Estimation of a not-to-exceed value or of a mean contaminant concentration, 

5. Pre-established or unknown cleanup standards and BTVs, and 

6. Sampling distributions (parametric or nonparametric) of the concentration data sets 

collected from the site and background areas under investigation. 

In background versus site comparison evaluations, the environmental population parameters of interest 

may include: 

• Preliminary remediation goals (PRGs), 

• Soil screening levels (SSLs), 

• RBC standards, 

• BTVs, not-to-exceed values, and 

• Compliance limit, maximum concentration limit (MCL), or alternative concentration 

limit (ACL), frequently used in groundwater applications. 

When the environmental parameters listed above are not known or pre-established, appropriate upper 

statistical limits are used to estimate those parameters. The UPL, UTL, and upper percentiles are used to 

estimate the BTVs and not-to-exceed values. Depending upon the site data availability, point-by-point site 

observations are compared with the estimated (or pre-established) BTVs and not-to-exceed values. If 

enough site and background data are available, two-sample hypotheses testing approaches are used to 

compare site concentrations with background concentrations levels. These statistical methods can also be 

used to compare contaminant concentrations of two site AOCs, surface and subsurface contaminant 

concentrations, or upgradient versus monitoring well contaminant concentrations. 

ProUCL Version 4.0 (ProUCL 4.0) is an upgrade of ProUCL Version 3.0 (EPA, 2004). ProUCL 4.0 

contains statistical methods to address various environmental issues for both full data sets without 

nondetects and for data sets with NDs (also known as left-censored data sets). 

ProUCL 4.0 contains: 

1. Rigorous parametric and nonparametric (including bootstrap methods) statistical methods 

(instead of simple ad hoc or substitution methods) that can be used on full data sets 

without nondetects and on data sets with below detection limit (BDL) or ND 

observations. 

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2. State-of-the-art parametric and nonparametric UCL, UPL, and UTL computation 

methods. These methods can be used on full-uncensored data sets without nondetects and 

also on data sets with BDL observations. Some of the methods (e.g., Kaplan-Meier 

method, ROS methods) are applicable on left-censored data sets having multiple 

detection limits. The UCL and other upper limit computation methods cover a wide range 

of skewed data sets with and without the BDLs. 

3. Single sample (e.g., Student’s t-test, sign test, proportion test, Wilcoxon Singed Rank 

test) and two-sample (Student’s t-test, Wilcoxon-Mann-Whitney test, Gehan test, quantile 

test) parametric and nonparametric hypotheses testing approaches for data sets with and 

without ND observations. These hypothesis testing approaches can be used to: verify the 

attainment of cleanup standards, perform site versus background comparisons, and 

compare two or more AOCs, monitoring wells (MWs). 

4. The single sample hypotheses testing approaches are used to compare site mean, site 

median, site proportion, or a site percentile (e.g., 95th) to a compliance limit (action level, 

regularity limit). The hypotheses testing approaches can handle both full-uncensored data 

sets without nondetects, and left-censored data sets with nondetects. Simple two-sample 

hypotheses testing methods to compare two populations are available in ProUCL 4.0, 

such as two-sample t-tests, Wilcoxon-Mann-Whitney (WMW) Rank Sum test, quantile 

test, Gehan’s test, and dispersion test. Variations of hypothesis testing methods (e.g., 

Levene’s method to compare dispersions, generalized WRS test) are easily available in 

most commercial and freely available software packages (e.g., MINITAB, R). 

5. ProUCL 4.0 also includes graphical methods (e.g., box plots, multiple Q-Q plots, 

histogram) to compare two or more populations. ProUCL 4.0 can also be used to display 

a box plot of one population (e.g., site data) with compliance limits or upper limits (e.g., 

UPL) of other population (background area) superimposed on the same graph. This kind 

of graph provides a useful visual comparison of site data with a compliance limit or 

BTVs. Graphical displays of a data set (e.g., Q-Q plot) should be used to gain insight 

knowledge contained in a data set that may not otherwise be clear by looking at simple 

test statistics such as t-test, Dixon test statistic, or Shapiro-Wilk (S-W) test statistic. 

6. ProUCL 4.0 can process multiple contaminants (variables) simultaneously and has the 

capability of processing data by groups. A valid group column should be included in the 

data file. 

7. ProUCL 4.0 provides GOF test for data sets with nondetects. The user can create 

additional columns to store extrapolated (estimated) values for nondetects based upon 

normal ROS, gamma ROS, and lognormal ROS (robust ROS) methods. 

ProUCL 4.0 retains all of the capabilities of ProUCL 3.0, including goodness-of-fit (GOF) tests for a 

normal, lognormal, and a gamma distribution and computation of UCLs based upon full data sets without 

nondetects. Graphical displays and GOF tests for data sets with BDL observations have also been 

included in ProUCL 4.0. It is re-emphasized that the computation of appropriate UCLs, UPLs, and other 

limits is based upon the assumption that the data set under study represents a single a single population. 

This means that the data set used to compute the limits should represent a single statistical population. For 

example, a background data set should represent a defensible background data set free of outlying 

observations. ProUCL 4.0 includes simple and commonly used classical outlier identification procedures, 

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such as the Dixon test and the Rosner test. These procedures are included as an aid to identify outliers. 

These simple classical outlier tests often suffer from masking effects in the presence of multiple outliers. 

Description and use of robust and resistant outlier procedures is beyond the scope of ProUCL 4.0. 

It is suggested that the classical outlier procedures should always be accompanied by graphical displays 

including box plots and Q-Q plots. The use of a Q-Q plot is useful to identify multiple or mixture samples 

that might be present in a data set. However, the decision regarding the proper disposition of outliers (e.g., 

to include or not to include outliers in statistical analyses; or to collect additional verification samples) 

should be made by members of the project team and experts familiar with site and background conditions. 

Guidance on the disposition of outliers and their accommodation in a data set by using a transformation 

(e.g., lognormal distribution) is discussed in Chapter 1 of this Technical Guide. 

ProUCL 4.0 has improved graphical methods, which may be used to compare the concentrations of two or 

more populations such as: 

1. Site versus background populations, 

2. Surface versus subsurface concentrations, 

3. Concentrations of two or more AOCs, and 

4. Identification of mixture samples and/or potential outliers 

These graphical methods include multiple quantile-quantile (Q-Q) plots, side-by-side box plots, and 

histograms. Whenever possible, it is desirable to supplement statistical results with useful visual displays 

of data sets. There is no substitute for graphical displays of a data set. For example, in addition to 

providing information about the data distribution, a normal Q-Q plot can also help identify outliers and 

multiple populations that may be present in a data set. On a Q-Q plot, observations well separated from 

the majority of the data may represent potential outliers, and jumps and breaks of significant magnitude 

may suggest the presence of observations from multiple populations in the data set. It is suggested that 

analytical outlier tests (e.g., Rosner test) and goodness-of-fit (G.O.F.) tests (e.g., SW test) should always 

be supplemented with the graphical displays such as Q-Q plot and box plot. 

ProUCL 4.0 serves as a companion software package for the Calculating Upper Confidence Limits for 

Exposure Point Concentrations at Hazardous Waste Sites (EPA, 2002a) and the Guidance for Comparing 

Background and Chemical Concentrations in Soil for CERCLA Sites (EPA, 2002b). ProUCL 4.0 is also 

useful to verify the attainment of cleanup standards (EPA, 1989). ProUCL 4.0 can also be used to perform 

two-sample hypotheses tests and to compute various upper limits often needed in groundwater monitoring 

applications (EPA, 1992 and EPA, 2004). 

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Acronyms and Abbreviations 

ACL alternative concentration limit 

A-D, AD Anderson-Darling test 

AM arithmetic mean 

AOC area(s) of concern 

BC Box-Cox transformation 

BCA bias-corrected accelerated bootstrap method 

BD binomial distribution 

BDL below detection limit 

BTV background threshold value 

CC confidence coefficient 

CDF, cdf cumulative distribution function 

CERCLA Comprehensive Environmental Recovery, Compensation, and Liability Act 

CL compliance limit 

CLT central limit theorem 

CMLE Cohen’s maximum likelihood estimate 

COPC contaminant(s) of potential concern 

CS cleanup standards 

CV coefficient of variation 

DCGL Design Concentration Guideline Level 

df degrees of freedom 

DL, L detection limit 

DL/2 (t) UCL based upon DL/2 method using Student’s t-distribution cutoff value 

DL/2 Estimates estimates based upon data set with nondetects replaced by half of the respective 

detection limits 

DOE Department of Energy 

DQO data quality objectives 

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EA exposure area 

EDF empirical distribution function 

EM expectation maximization 

EU exposure unit 

EPA Environmental Protection Agency 

EPC exposure point concentration 

GOF, G.O.F. goodness-of-fit 

H-UCL UCL based upon Land’s H-statistic 

IQR interquartile range 

KM (%) UCL based upon Kaplan-Meier estimates using the percentile bootstrap method 

KM (Chebyshev) UCL based upon Kaplan-Meier estimates using the Chebyshev inequality 

KM (t) UCL based upon Kaplan-Meier estimates using the Student’s t-distribution cutoff value 

KM (z) UCL based upon Kaplan-Meier estimates using standard normal distribution cutoff 

value 

K-M, KM Kaplan-Meier 

K-S, KS Kolmogorov-Smirnov 

LBGR Lower Bound of the Gray Region 

LN lognormal distribution 

MAD median absolute deviation 

MCL maximum concentration limit, maximum compliance limit 

MDD minimum detectable difference 

ML maximum likelihood 

MLE maximum likelihood estimate 

MLE (t) UCL based upon maximum likelihood estimates using Student’s t-distribution cutoff 

value 

MLE (Tiku) UCL based upon maximum likelihood estimates using the Tiku’s method 

Multi Q-Q multiple quantile-quantile plot 

x 
MV minimum variance 

MVUE minimum variance unbiased estimate 

MW monitoring well 

ND nondetect 

NRC Nuclear Regulatory Commission 

OLS ordinary least squares 

ORD Office of Research and Development 

PDF, pdf probability density function 

PLE product limit estimate 

PRG preliminary remediation goals 

Q-Q quantile-quantile 

RBC risk-based cleanup 

RCRA Resource Conservation and Recovery Act 

RL reporting limit 

RMLE restricted maximum likelihood estimate 

ROS regression on order statistics 

RSD relative standard deviation 

RU remediation unit 

RV random variable 

S substantial difference 

SD, Sd, sd standard deviation 

SE Standard error 

SND standard normal distribution 

SSL soil screening levels 

S-W, SW Shapiro-Wilk 

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U.S. EPA, USEPA United States Environmental Protection Agency 

UCL upper confidence limit 

UCL95 a 95% upper confidence limit 

UMLE unbiased maximum likelihood estimate method 

UPL upper prediction limit 

UTL upper tolerance limit 

WMW Wilcoxon-Mann-Whitney 

WRS Wilcoxon Rank Sum 

WSR Wilcoxon Signed Rank 

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Glossary 

Anderson-Darling (AD) test: The Anderson-Darling test assesses whether known data come from a 

specified distribution. 

Background Measurements: The measurements that are not related to the site. Background sources can 

be naturally occurring or anthropogenic (man-made). 

Bias: The systematic or persistent distortion of a measured value from its true value (this can occur 

during sampling design, the sampling process, or laboratory analysis). 

Bootstrap Method: The bootstrap method is a computer-based method for assigning measures of 

accuracy to sample estimates. This technique allows estimation of the sample distribution of almost any 

statistic using only very simple methods. Bootstrap methods are generally superior to ANOVA for small 

data sets or where sample distributions are non-normal. 

Central Limit Theorem (CLT): The central limit theorem states that given a distribution with a mean μ

and variance σ

2

, the sampling distribution of the mean approaches a normal distribution with a mean (μ) 

and a variance σ

2

/N as N, the sample size, increases. 

Coefficient of Variation (CV): A dimensionless quantity used to measure the spread of data relative to 

the size of the numbers. For a normal distribution, the coefficient of variation is given by s/xBar. Also 

known as the relative standard deviation (RSD). 

Confidence Coefficient: The confidence coefficient (a number in the closed interval [0, 1]) associated 

with a confidence interval for a population parameter is the probability that the random interval 

constructed from a random sample (data set) contains the true value of the parameter. The confidence 

coefficient is related to the significance level of an associated hypothesis test by the equality: level of 

significance = 1 – confidence coefficient. 

Confidence Interval: Based upon the sampled data set, a confidence interval for a parameter is a random 

interval within which the unknown population parameter, such as the mean, or a future observation, x0, 

falls. 

Confidence Limit: The lower or an upper boundary of a confidence interval. For example, the 95% upper 

confidence limit (UCL) is given by the upper bound of the associated confidence interval. 

Coverage, Coverage Probability: The coverage probability (e.g., = 0.95) of an upper confidence limit 

(UCL) of the population mean represents the confidence coefficient associated with the UCL. 

Data Quality Objectives (DQOs): Qualitative and quantitative statements derived from the DQO 

process that clarify study technical and quality objectives, define the appropriate type of data, and specify 

tolerable levels of potential decision errors that will be used as the basis for establishing the quality and 

quantity of data needed to support decisions. 

Detection Limit: A measure of the capability of an analytical method to distinguish samples that do not 

contain a specific analyte from samples that contain low concentrations of the analyte. The lowest 

concentration or amount of the target analyte that can be determined to be different from zero by a single 

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measurement at a stated level of probability. Detection limits are analyte- and matrix-specific and may be 

laboratory-dependent. 

Empirical Distribution Function (EDF): In statistics, an empirical distribution function is a cumulative 

probability distribution function that concentrates probability 1/n at each of the n numbers in a sample. 

Estimate: A numerical value computed using a random data set (sample), and is used to guess (estimate) 

the population parameter of interest (e.g., mean). For example, a sample mean represents an estimate of 

the unknown population mean. 

Expectation Maximization (EM): The EM algorithm is used to approximate a probability function (p.f. 

or p.d.f.). EM is typically used to compute maximum likelihood estimates given incomplete samples. 

Exposure Point Concentration (EPC): The contaminant concentration within an exposure unit to which 

the receptors are exposed. Estimates of the EPC represent the concentration term used in exposure 

assessment. 

Extreme Values: The minimum and the maximum values. 

Goodness-of-Fit (GOF): In general, the level of agreement between an observed set of values and a set 

wholly or partly derived from a model of the data. 

Gray Region: A range of values of the population parameter of interest (such as mean contaminant 

concentration) within which the consequences of making a decision error are relatively minor. The gray 

region is bounded on one side by the action level. The width of the gray region is denoted by the Greek 

letter delta in this guidance. 

H-Statistic: The unique symmetric unbiased estimator of the central moment of a distribution. 

H-UCL: UCL based on Land’s H-Statistic. 

Hypothesis: Hypothesis is a statement about the population parameter(s) that may be supported or 

rejected by examining the data set collected for this purpose. There are two hypotheses: a null hypothesis, 

(H0), representing a testable presumption (often set up to be rejected based upon the sampled data), and an 

alternative hypothesis (HA), representing the logical opposite of the null hypothesis. 

Jackknife Method: A statistical procedure in which, in its simplest form, estimates are formed of a 

parameter based on a set of N observations by deleting each observation in turn to obtain, in addition to 

the usual estimate base d on N observations, N estimates each based on N-1 observations. 

Kolmogorov-Smirnov (KS) test: The Kolmogorov-Smirnov test is used to decide if a sample comes 

from a population with a specific distribution. The Kolmogorov-Smirnov test is based on the empirical 

distribution function (EDF). 

Level of Significance: The error probability (also known as false positive error rate) tolerated of falsely 

rejecting the null hypothesis and accepting the alternative hypothesis. 

Lilliefors test: A test of normality for large data sets when the mean and variance are unknown. 

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Maximum Likelihood Estimates (MLE): Maximum likelihood estimation (MLE) is a popular statistical 

method used to make inferences about parameters of the underlying probability distribution of a given 

data set. 

Mean: The sum of all the values of a set of measurements divided by the number of values in the set; a 

measure of central tendency. 

Median: The middle value for an ordered set of n values. Represented by the central value when n is odd 

or by the average of the two most central values when n is even. The median is the 50th percentile. 

Minimum Detectable Difference (MDD): The minimum detectable difference (MDD) is the smallest 

difference in means that the statistical test can resolve. The MDD depends on sample-to-sample 

variability, the number of samples, and the power of the statistical test. 

Minimum Variance Unbiased Estimates (MVUE): A minimum variance unbiased estimator (MVUE or 

MVU estimator) is an unbiased estimator of parameters, whose variance is minimized for all values of the 

parameters. If an estimator is unbiased, then its mean squared error is equal to its variance. 

Nondetect (ND): Censored data values. 

Nonparametric: A term describing statistical methods that do not assume a particular population 

probability distribution, and are therefore valid for data from any population with any probability 

distribution, which can remain unknown. 

Optimum: An interval is optimum if it possesses optimal properties as defined in the statistical literature. 

This may mean that it is the shortest interval providing the specified coverage (e.g., 0.95) to the 

population mean. For example, for normally distributed data sets, the UCL of the population mean based 

upon Student’s t distribution is optimum. 

Outlier: Measurements (usually larger or smaller than the majority of the data values in a sample) that 

are not representative of the population from which they were drawn. The presence of outliers distorts 

most statistics if used in any calculations. 

p-value: In statistical hypothesis testing, the p-value of an observed value tobserved of some random 

variable T used as a test statistic is the probability that, given that the null hypothesis is true, T will 

assume a value as or more unfavorable to the null hypothesis as the observed value tobserved. 

Parameter: A parameter is an unknown constant associated with a population. 

Parametric: A term describing statistical methods that assume a normal distribution. 

Population: The total collection of N objects, media, or people to be studied and from which a sample is 

to be drawn. The totality of items or units under consideration. 

Prediction Interval: The interval (based upon historical data, or a background well) within which a 

newly and independently obtained (often labeled as a future observation) site observation (from a 

compliance well) of the predicted variable (lead) falls with a given probability (or confidence coefficient). 

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Probability of Type 2 Error (=β): The probability, referred to as β (beta), that the null hypothesis will 

not be rejected when in fact it is false (false negative). 

Probability of Type I Error = Level of Significance (= α): The probability, referred to as α (alpha), that 

the null hypothesis will be rejected when in fact it is true (false positive). 

pth Percentile: The specific value, Xp of a distribution that partitions a data set of measurements in such a 

way that the p percent (a number between 0 and 100) of the measurements fall at or below this value, and 

(100-p) percent of the measurements exceed this value, Xp.) 

pth Quantile: The specific value of a distribution that divides the set of measurements in such a way that 

the proportion, p, of the measurements falls below (or are equal to) this value, and the proportion (1-p) of 

the measurements exceed this value. 

Quality Assurance: An integrated system of management activities involving planning, implementation, 

assessment, reporting, and quality improvement to ensure that a process, item, or service is of the type 

and quality needed and expected by the client. 

Quality Assurance Project Plan: A formal document describing, in comprehensive detail, the necessary 

QA, QC, and other technical activities that must be implemented to ensure that the results of the work 

performed will satisfy the stated performance criteria. 

Quantile Plot: A graph that displays the entire distribution of a data set, ranging from the lowest to the 

highest value. The vertical axis represents the measured concentrations, and the horizontal axis is used to 

plot the percentiles of the distribution. 

Range: The numerical difference between the minimum and maximum of a set of values. 

Regression on Order Statistics (ROS): A regression line is fit to the normal scores of the order 

statistics for the uncensored observations and then to fill in values extrapolated from the straight line for 

the observations below the detection limit. 

Resampling: The repeated process of obtaining representative samples and/or measurements of a 

population of interest. 

Reliable UCL: This is similar to a stable UCL. 

Robustness: Robustness is used to compare statistical tests. A robust test is the one with good 

performance (that is not unduly affected by outliers) for a wide variety of data distributions. 

Sample: A sample here represents a random sample (data set) obtained from the population of interest 

(e.g., a site area, a reference area, or a monitoring well). The sample is supposed to be a representative 

sample of the population under study. The sample is used to draw inferences about the population 

parameter(s). 

Shapiro-Wilk (SW) test: In statistics, the Shapiro-Wilk test tests the null hypothesis that a sample 

x1, ..., xn came from a normally distributed population. 

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Skewness: A measure of asymmetry of the distribution of the characteristic under study (e.g., lead 

concentrations). It can also be measured in terms of the standard deviation of log-transformed data. The 

higher is the standard deviation, the higher is the skewness. 

Stable UCL: The UCL of a population mean is a stable UCL if it represents a number of practical merits, 

which also has some physical meaning. That is, a stable UCL represents a realistic number (e.g., 

contaminant concentration) that can occur in practice. Also, a stable UCL provides the specified (at least 

approximately, as much as possible, as close as possible to the specified value) coverage (e.g., ~0.95) to 

the population mean. 

Standard Deviation (sd): A measure of variation (or spread) from an average value of the sample data 

values. 

Standard Error (SE): A measure of an estimate's variability (or precision). The greater the standard 

error in relation to the size of the estimate, the less reliable the estimate. Standard errors are needed to 

construct confidence intervals for the parameters of interests such as the population mean and population 

percentiles. 

Tolerance Limit: A confidence limit on a percentile of the population rather than a confidence limit on 

the mean. For example, a 95 percent one-sided TL for 95 percent coverage represents the value below 

which 95 percent of the population values are expected to fall with 95 percent confidence. In other words, 

a 95% UTL with coverage coefficient 95% represents a 95% upper confidence limit for the 95th 

percentile. 

Unreliable UCL, Unstable UCL, Unrealistic UCL: The UCL of a population mean is unstable, 

unrealistic, or unreliable if it is orders of magnitude higher than the other UCLs of population mean. It 

represents an impractically large value that cannot be achieved in practice. For example, the use of Land’s 

H statistic often results in impractically large inflated UCL value. Some other UCLs, such as the bootstrap 

t UCL and Hall’s UCL, can be inflated by outliers resulting in an impractically large and unstable value. 

All such impractically large UCL values are called unstable, unrealistic, unreliable, or inflated UCLs. 

Upper Confidence Limit (UCL): The upper boundary (or limit) of a confidence interval of a parameter 

of interest such as the population mean. 

Upper Prediction Limit (UPL): The upper boundary of a prediction interval for an independently 

obtained observation (or an independent future observation). 

Upper Tolerance Limit (UTL): The upper boundary of a tolerance interval. 

Winsorization method: The Winsorization method is a procedure that replaces the n extreme values with 

the preset cut-off value. This method is sensitive to the number of outliers, but not to their actual values. 

xvii 
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Table of Contents 

Executive Summary.................................................................................................................................v

Acronyms and Abbreviations ..............................................................................................................ix

Glossary ..................................................................................................................................................xiii

Introduction ...............................................................................................................................................1

The Need for ProUCL Software ............................................................................................. 1 

ProUCL 4.0 Capabilities ........................................................................................................ 3

 Graphical Capabilities........................................................................................................12

Chapter 1 Guidance on the Use of Statistical Methods as Incorporated in ProUCL 4.0 & 

Associated Minimum Sample Size Requirements ...........................................................13

1.1 Background Data Sets.......................................................................................................14 

1.2 Site Data Sets....................................................................................................................15 

1.3 Discrete Samples or Composite Samples?........................................................................16 

1.4 Upper Limits and Their Use .............................................................................................17 

1.4.1 Example 1-1.........................................................................................................17 

1.5 Point-by-Point Comparison of Site Observations with BTVs, 

Compliance Limits, and Other Threshold Values.............................................................19 

1.6 Hypothesis Testing Approaches and Their Use................................................................21 

1.6.1 Single Sample Hypotheses – BTVs and Not-to-Exceed Values are 

Known (Pre-established)......................................................................................21 

1.6.2 Two-Sample Hypotheses – When BTVs and Not-to-Exceed Values

are Unknown........................................................................................................22 

1.7 Minimum Sample Size Requirements ..............................................................................23 

1.7.1 Minimum Sample Size for Estimation and Point-by-Point Site 

Observation Comparisons....................................................................................24 

1.7.2 Minimum Sample Sizes for Hypothesis Testing .................................................24 

1.7.3 Sample Sizes for Bootstrap Methods...................................................................25 

1.8 Statistical Analyses by a Group ID...................................................................................25 

1.9 Use of Maximum Detected Value as Estimates of Upper Limits.....................................26 

1.9.1 Use of Maximum Detected Value to Estimate BTVs and 

Not-to-Exceed Values..........................................................................................26 

1.9.2 Use of Maximum Detected Value to Estimate EPC Terms.................................26 

1.10 Samples with Nondetect Observations .............................................................................28 

1.10.1 Avoid the Use of DL/2 Method to Compute UCL95 ..........................................28 

1.11 Samples with Low Frequency of Detection......................................................................28 

1.12 Some Other Applications of Methods in ProUCL 4.0......................................................29 

1.12.1 Identification of COPCs.......................................................................................29 

1.12.2 Identification of Non-Compliance Monitoring Wells.......................................... 30 

1.12.3 Verification of the Attainment of Cleanup Standards, Cs....................................30 

1.12.4 Using BTVs (Upper Limits) to Identify Hot Spots..............................................30 

xix 
Chapter 2 Methods for Computing (1 – α)100% UCL of Mean for Data Sets without 

Nondetect Observations as Incorporated in ProUCL 4.0 Software .............................33

2.1 Introduction.......................................................................................................................33 

2.2 Goodness-of-Fit (GOF) Test Procedures to Test for a Data Distribution.........................35 

2.2.1 Test Normality and Lognormality of a Data Set..................................................36 

2.2.1.1 Normal Quantile-Quantile (Q-Q) Plot ...................................................36 

2.2.1.2 Shapiro-Wilk W Test.............................................................................36 

2.2.1.3 Lilliefors Test..........................................................................................36 

2.2.2 Gamma Distribution ............................................................................................37 

2.2.2.1 Quantile-Quantile (Q-Q) Plot for a Gamma Distribution .......................37 

2.2.2.2 Empirical Distribution Function (EDF)-Based 

Goodness-of-Fit Tests.............................................................................38 

2.3 Estimation of Parameters of the Three Distributions as Incorporated in ProUCL............39 

2.3.1 Normal Distribution.............................................................................................39 

2.3.2.1 MLEs of the Parameters of a Lognormal Distribution ...........................40 

2.3.2.2 Relationship between Skewness and Standard Deviation, σ ..................40 

2.3.2.3 MLEs of the Quantiles of a Lognormal Distribution..............................41 

2.3.2.4 MVUEs of Parameters of a Lognormal Distribution..............................42 

2.3.2 Estimation of the Parameters of a Gamma Distribution ......................................42 

2.4 Methods for Computing a UCL of the Unknown Population Mean.................................44 

2.4.1 (1 – α)100% UCL of the Mean Based Upon Student’s t-Statistic .......................45 

2.4.2 Computation of the UCL of the Mean of a Gamma, G (k, θ), Distribution.........46 

2.4.3 (1 – α)100% UCL of the Mean Based Upon H-Statistic (H-UCL)......................47 

2.4.4 (1 – α)100% UCL of the Mean Based Upon Modified t-Statistic for 

Asymmetrical Populations...................................................................................48 

2.4.5 (1 – α)100% UCL of the Mean Based Upon the Central Limit Theorem............49 

2.4.6 (1 – α)100% UCL of the Mean Based Upon the Adjusted Central Limit 

Theorem (Adjusted-CLT)....................................................................................49 

2.4.7 Chebyshev (1 – α)100% UCL of the Mean Using Sample Mean 

and Sample sd).....................................................................................................50 

2.4.8 Chebyshev (1 – α)100% UCL of the Mean of a Lognormal Population 

Using the MVUE of the Mean and its Standard Error.........................................51 

2.4.9 (1 – α)100% UCL of the Mean Using the Jackknife and Bootstrap Methods.....52 

2.4.9.1 (1 – α)100% UCL of the Mean Based Upon the Jackknife Method.......53 

2.4.9.2 (1 – α)100% UCL of the Mean Based Upon the Standard 

Bootstrap Method ...................................................................................54 

2.4.9.3 (1 – α)100% UCL of the Mean Based Upon the Simple Percentile 

Bootstrap Method ...................................................................................55 

2.4.9.4 (1 – α)100% UCL of the Mean Based Upon the Bias-Corrected 

Accelerated (BCA) Percentile Bootstrap Method...................................55 

2.4.9.5 (1 – α)100% UCL of the Mean Based Upon the Bootstrap t Method.....56 

2.4.9.6 (1 – α)100% UCL of the Mean Based Upon Hall’s 

Bootstrap Method ...................................................................................57 

2.5 Recommendations and Summary .....................................................................................58 

2.5.1 Recommendations to Compute a 95% UCL of the Unknown Population 

Mean, μ1, Using Symmetric and Positively Skewed Data Sets...........................59 

2.5.1.1 Normally or Approximately Normally Distributed Data Sets.................59 

2.5.1.2 Gamma Distributed Skewed Data Sets...................................................59 

2.5.1.3 Lognormally Distributed Skewed Data Sets...........................................61 

xx 
2.5.1.4 Nonparametric Distribution-Free Skewed Data Sets without a 

Discernable Distribution.........................................................................64 

2.5.2 Summary of the Procedure to Compute a 95% UCL of the Unknown Population 

Mean, μ1, Based Upon Full Data Sets without Nondetect Observations.............65 

Chapter 3 Estimating Background Threshold Values or Establishing Site-Specific 

Background Concentrations Using Full Data Sets without Nondetect 

(ND) Observations ....................................................................................................................69

3.1 Introduction.......................................................................................................................69 

3.2 Treatment of Outliers........................................................................................................71 

3.3 Upper p*100% Percentiles as Estimates of Background Threshold Values (BTVs).......73 

3.3.1 Nonparametric p*100% Percentile ......................................................................73 

3.3.2 Normal p*100% Percentile..................................................................................73 

3.3.3 Lognormal p*100% Percentile ............................................................................74 

3.3.4 Gamma p*100% Percentile .................................................................................74 

3.3.5 Example 1 ............................................................................................................75 

3.3.5.1 Normal Percentile ...................................................................................75 

3.3.5.2 Lognormal Percentile..............................................................................76 

3.3.5.3 Nonparametric Percentile .......................................................................76 

3.3.5.4 Gamma Percentile...................................................................................76 

3.4 Upper Tolerance Limits....................................................................................................78 

3.4.1 Normal Upper Tolerance Limits..........................................................................78 

3.4.2 Lognormal Upper Tolerance Limits ....................................................................79 

3.4.3 Gamma Distribution Upper Tolerance Limits.....................................................79 

3.4.4 Nonparametric Upper Tolerance Limits..............................................................79 

3.4.5 Example 2: Computation of Upper Tolerance Limits..........................................80 

3.4.5.1 Normal Upper Tolerance Limits.............................................................80 

3.4.5.2 Lognormal Upper Tolerance Limits.......................................................81 

3.4.5.3 Nonparametric Upper Tolerance Limits.................................................81 

3.5 Nonparametric Upper Limit Based Upon Interquartile Range (IQR) – IQR 

Upper Limit.......................................................................................................................82 

3.5.1 Example 3: IQR Upper Limit .............................................................................. 82 

3.6 Upper Prediction Limits....................................................................................................82 

3.6.1 Normal Upper Prediction Limit...........................................................................83 

3.6.2 Lognormal Upper Prediction Limit .....................................................................83 

3.6.3 Nonparametric Upper Prediction Limit ...............................................................83 

3.6.4 Example 4 ............................................................................................................83 

3.6.4.1 Normal Upper Prediction Limit..............................................................84 

3.6.4.2 Lognormal Upper Prediction Limit ........................................................84 

3.6.4.3 Nonparametric Upper Prediction Limit ..................................................84 

Chapter 4 Computation of Upper Confidence Limit of the Population Mean Based 

Upon Data Sets with Nondetect Observations..................................................................89

4.1 Introduction.......................................................................................................................89 

4.2 Pre-processing a Data Set .................................................................................................90 

4.2.1 Handling of Outliers ............................................................................................90 

4.2.2 Disposition of Outliers.........................................................................................91 

4.2.3 Assessing Influence of Outliers...........................................................................91 

xxi 
4.2.4 Log-Transformation Tends to Accommodate Outliers 

(and Contamination) ............................................................................................91 

4.2.4.1 Avoid Data Transformations ..................................................................91 

4.2.4.2 Do Not Use DL/2 (t) UCL Method.........................................................92 

4.2.5 Minimum Data Requirement ...............................................................................92 

4.3 Estimation of Population Mean and Variance Based Upon Left-Censored 

Data Sets...........................................................................................................................92 

4.3.1 Goodness-of-Fit (GOF) Tests for Data Sets with Nondetect Observations.........93 

4.3.2 Regression on Order Statistics (ROS) Estimation Methods ................................94 

4.3.3 OLS Regression Line Based Upon Detected Data ..............................................94 

4.3.4 Computation of the Plotting Positions (Percentiles) and Quantiles.....................95 

4.3.5 ROS Estimates Obtained Using Only Detected Observations.............................95 

4.3.5.1 ROS Method for Normal Distribution....................................................96 

4.3.5.2 ROS Method for Lognormal Distribution ..............................................96 

4.3.5.3 Robust ROS Method on Log-Transformed Data ....................................97 

4.3.5.4 Gamma ROS Method .............................................................................98 

4.4 Saving Extrapolated Nondetect Values Using ROS Est. with NDs Option of 

ProUCL 4.0.....................................................................................................................100 

4.5 Influence of Outliers on ROS methods...........................................................................100 

4.6 Nonparametric Kaplan-Meier (KM) Estimation Method ...............................................100 

4.7 Bootstrap UCL Computation Methods for Left-Censored Data Sets .............................102 

4.7.1 Bootstrapping Data Sets with Nondetect Observations.....................................102 

4.7.1.1 UCL of Mean Based Upon Standard Bootstrap Method ......................104 

4.7.1.2 UCL of Mean Based Upon Bootstrap t Method ...................................104 

4.7.1.3 Percentile Bootstrap Method ................................................................ 105 

4.7.1.4 Bias-Corrected Accelerated (BCA) Percentile Bootstrap Procedure....105 

4.8 Additional UCL Computation Methods for Left-Censored Data Sets............................106 

4.8.1 Ad hoc UCL95 Computation Method Based Upon Student’s t-distribution .....106 

4.8.2 (1 – α)100% UCL Based Upon Chebyshev Theorem Using 

Sample Mean and Sd..........................................................................................106 

4.8.3 UCL95 Based Upon Tiku’s Method (for symmetrical censoring) ....................107 

4.9 Comments on the Use of Substitution Methods and Minimum Sample 

Size Requirements ..........................................................................................................107 

4.9.1 Use of Ad hoc Estimation Methods on Case-by-Case Basis.............................108 

4.10 Summary and Recommendations ...................................................................................108 

4.10.1 General Observations and Comments................................................................109 

4.10.2 Recommended UCL95 Methods for Normal (Approximate 

Normal) Distribution..........................................................................................111 

4.10.3 Recommended UCL95 Methods for Gamma Distribution................................111 

4.10.4 Recommended UCL95 Methods for Lognormal Distribution...........................112 

4.10.5 Recommended UCL95 Methods for Non-Discernable Distributions................113 

Chapter 5 Estimating Background Threshold Values and Establishing Site-Specific 

Background Concentrations Using Data Sets with Nondetect 

(ND) Observations ..................................................................................................................115

5.1 Introduction....................................................................................................................115 

5.2 Underlying Assumptions ................................................................................................116 

5.3 Identification of High (in Upper Tail) Outliers for Left-Censored Data Sets.................117 

5.3.1 Outlier Testing Procedures for Data Sets with NDs..........................................118 

xxii 
5.3.2 Q-Q Plots for Data Sets with Nondetect Observations......................................118 

5.4 Estimating BTVs and Not-to-Exceed Values Based Upon Left-Censored 

Data Sets.........................................................................................................................118 

5.4.1 Computing Upper Prediction Limits (UPLs) for Left-Censored Data Sets.......119 

5.4.1.1 UPLs Based Upon Student’s t-type Statistic ........................................119 

5.4.1.2 UPL Based Upon the Chebyshev Inequality ........................................120 

5.4.1.3 UPLs Based Upon ROS Methods.........................................................120 

5.4.2 Computing Upper p*100% Percentiles for Left-Censored Data Sets................121 

5.4.2.1 Upper Percentiles Based Upon Standard Normal Z-Scores..................121 

5.4.2.2 Upper Percentiles Based Upon ROS Methods......................................121 

5.4.3 Computing Upper Tolerance Limits (UTLs) for Left-Censored Data Sets .......121 

5.4.3.1 UTLs Based Upon K Critical Values Obtained Using a 

Non-Central t-Distribution....................................................................121 

5.4.3.2 UTLs Based Upon ROS Methods.........................................................122 

5.5 Estimating BTVs Using Nonparametric Methods Based Upon Higher Order 

Statistics..........................................................................................................................122 

5.5.1 Using Ad hoc Estimation Methods on Case-by-Case Basis..............................122 

5.5.2 Example 1 ..........................................................................................................123 

5.6 Minimum Sample Size Requirements ............................................................................130 

5.7 Additional Suggestions and Comments..........................................................................131 

Chapter 6 Single and Two-Sample Hypotheses Testing Approaches as Incorporated 

in ProUCL 4.0 ...........................................................................................................................133

6.1 When to Use Single Sample Hypotheses Approaches....................................................133 

6.2 When to Use Two-Sample Hypotheses Testing Approaches .........................................134 

6.3 Statistical Terminology Used in Hypotheses Testing Approaches.................................136 

6.3.1 Test Form 1........................................................................................................140 

6.3.2 Test Form 2........................................................................................................141 

6.3.3 Selecting a Test Form ........................................................................................141 

6.3.4 Errors Tests and Confidence Levels .................................................................. 142 

6.4 Parametric Hypotheses Tests..........................................................................................143 

6.5 Nonparametric Hypotheses Tests ...................................................................................144 

6.6 Single Sample Hypotheses Testing Approaches.............................................................145 

6.6.1 The One-Sample t-Test......................................................................................145 

6.6.1.1 Limitations and Robustness..................................................................145 

6.6.1.2 Directions for the One-Sample t-Test...................................................146 

6.6.2 The One-Sample Test for Proportions...............................................................147 

6.6.2.1 Limitations and Robustness..................................................................147 

6.6.2.2 Directions for the One-Sample Test for Proportions............................147 

6.6.3 The Sign Test.....................................................................................................150 

6.6.3.1 Limitations and Robustness..................................................................150 

6.6.3.2 Sign Test in the Presence of Nondetects...............................................150 

6.6.3.3 Directions for the Sign Test..................................................................150 

6.6.4 The Wilcoxon Signed Rank Test.......................................................................152 

6.6.4.1 Limitations and Robustness..................................................................152 

6.6.4.2 Wilcoxon Signed Rank (WSR) Test in the Presence of Nondetects.....152 

6.6.4.3 Directions for the Wilcoxon Signed Rank Test ....................................152 

6.7 Two-Sample Hypotheses Testing Approaches...............................................................153 

6.7.1 Student’s Two-Sample t-Test (Equal Variances) ..............................................154 

xxiii 
6.7.1.1 Assumptions and Their Verification.....................................................154 

6.7.1.2 Limitations and Robustness..................................................................154 

6.7.1.3 Guidance on Implementing the Student’s Two-Sample t-Test.............155 

6.7.1.4 Directions for the Student’s Two-Sample t-Test ..................................155 

6.7.2 The Satterthwaite Two-Sample t-Test (Unequal Variances).............................156 

6.7.2.1 Limitations and Robustness..................................................................156 

6.7.2.2 Directions for the Satterthwaite Two-Sample t-Test ............................157 

6.8 Tests for Equality of Dispersions....................................................................................158 

6.8.1 The F-Test for the Equality of Two-Variance ...................................................158 

6.8.1.1 Directions for the F-Test.......................................................................158 

6.9 Nonparametric Tests.......................................................................................................159 

6.9.1 The Wilcoxon-Mann-Whitney (WMW) Test ....................................................159 

6.9.1.1 Advantages and Disadvantages ............................................................160 

6.9.1.2 WMW Test in the Presence of Nondetects...........................................161 

6.9.1.3 WMW Test Assumptions and Their Verification.................................161 

6.9.1.4 Directions for the WMW Test when the Number of 

Site and Background Measurements is small (n # 20 and m # 20) ......161 

6.9.1.5 Directions for the WMW Test when the Number of 

Site and Background Measurements is Large (n > 20 and m > 20) .....163 

6.9.2 The Gehan Test..................................................................................................165 

6.9.2.1 Limitations and Robustness..................................................................165 

6.9.2.2 Directions for the Gehan Test when m ≥ 10 and n ≥ 10. ......................165 

6.9.3 The Quantile Test...............................................................................................167 

6.9.3.1 Limitations and Robustness..................................................................167 

6.9.3.2 Quantile Test in the Presence of Nondetects ........................................167 

6.9.3.3 Directions for the Quantile Test............................................................167 

Chapter 7 Outlier Tests for Data Sets with and without Nondetect Values....................... 171

7.1 Outlier Tests for Data Sets without Nondetect Observations.........................................173 

7.1.1 Dixon’s Test.......................................................................................................173 

7.1.1.1 Directions for the Dixon’s Test ............................................................173 

7.1.2 Rosner’s Test .....................................................................................................174 

7.1.2.1 Directions for the Rosner’s Test ...........................................................174 

7.2 Outlier Tests for Data Sets with Nondetect Observations ..............................................175 

Appendix ................................................................................................................................................183

Simulated Critical Values for Gamma GOF Tests, the Anderson-Darling Test, 

and the Kolmogorov-Smirnov Test ....................................................................................183

References.............................................................................................................................................193

xxiv 
xxv

Introduction 

The Need for ProUCL Software 

Statistical inferences about the sampled populations and their parameters are made based upon defensible 

and representative data sets of appropriate sizes collected from the populations under investigation. 

Statistical inference, including both estimation and hypotheses testing approaches, is routinely used to: 

1. Estimate environmental parameters of interest such as exposure point concentration 

(EPC) terms, not-to-exceed values, and background level threshold values (BTVs) for 

contaminants of potential concern (COPC), 

2. Identify areas of concern (AOC) at a contaminated site, 

3. Compare contaminant concentrations found at two or more AOCs of a contaminated site, 

4. Compare contaminant concentrations found at an AOC with background or reference 

area contaminant concentrations, 

5. Compare site concentrations with a cleanup standard to verify the attainment of cleanup 

standards. 

Statistical inference about the sampled populations and their parameters are made based upon defensible 

and representative data sets of appropriate sizes collected from the populations under investigation. 

Environmental data sets originated from the Superfund and RCRA sites often consist of observations 

below one or more detection limits (DLs). In order to address the statistical issues arising in exposure and 

risk assessment applications; background versus site comparison and evaluation studies; and various other 

environmental applications, several graphical, parametric, and nonparametric statistical methods for data 

sets with nondetects and without nondetects have been incorporated into ProUCL Version 4.0 (ProUCL 

4.0). 

Exposure and risk management and cleanup decisions in support of United States Environmental 

Protection Agency (EPA) projects are often made based upon the mean concentrations of the COPCs. A 

95% upper confidence limit (UCL95) of the unknown population (e.g., an AOC) arithmetic mean (AM), 

μ1, can be used to: 

• Estimate the EPC term of the AOC under investigation, 

• Determine the attainment of cleanup standards, 

• Compare site mean concentrations with reference area mean concentrations, and 

• Estimate background level mean contaminant concentrations. The background mean 

contaminant concentration level may be used to compare the mean of an AOC. It should 

be noted that it is not appropriate to compare individual point-by-point site observations 

with the background mean concentration level. 

It is important to compute a reliable and stable UCL95 of the population mean using the available data. 

The UCL95 should approximately provide the 95% coverage for the unknown population mean, μ1. Based 

upon the available background data, it is equally important to compute reliable and stable upper 

percentiles, upper prediction limits (UPLs), or upper tolerance limits (UTLs). These upper limits based 

upon background (or reference) data are used as estimates of BTVs, compliance limits (CL), or not-to1 
exceed values. These upper limits are often used in site (point-by-point) versus background comparison 

evaluations. 

Environmental scientists often encounter trace level concentrations of COPCs when evaluating sample 

analytical results. Those low level analytical results cannot be measured accurately, and therefore are 

typically reported as less than one or more detection limit (DL) values (also called nondetects). However, 

practitioners often need to obtain reliable estimates of the population mean, μ1, the population standard 

deviation, σ1, and upper limits, including the upper confidence limit (UCL) of the population mass or 

mean, the UPL, and the UTL based upon data sets with nondetect (ND) observations. Hypotheses testing 

approaches are often used to verify the attainment of cleanup standards, and compare site and background 

concentrations of COPCs. 

Background evaluation studies, BTVs, and not-to-exceed values should be estimated based upon 

defensible background data sets. The estimated BTVs or not-to-exceed values are then used to identify the 

COPCs, to identify the site AOCs or hot spots, and to compare the contaminant concentrations at a site 

with background concentrations. The use of appropriate statistical methods and limits for site versus 

background comparisons is based upon the following factors: 

1. Objective of the study, 

2. Environmental medium (e.g., soil, groundwater, sediment, air) of concern, 

3. Quantity and quality of the available data, 

4. Estimation of a not-to-exceed value or of a mean contaminant concentration, 

5. Pre-established or unknown cleanup standards and BTVs, and 

6. Sampling distributions (parametric or nonparametric) of the concentration data sets 

collected from the site and background areas under investigation. 

In background versus site comparison evaluations, the environmental population parameters of interest 

may include: 

• Preliminary remediation goals (PRGs), 

• Soil screening levels (SSLs), 

• Risk-based cleanup (RBC) standards, 

• BTVs, not-to-exceed values, and 

• Compliance limit, maximum concentration limit (MCL), or alternative concentration 

limit (ACL), frequently used in groundwater applications. 

When the environmental parameters listed above are not known or have not been pre-established, 

appropriate upper statistical limits are used to estimate the parameters. The UPL, UTL, and upper 

percentiles are used to estimate the BTVs and not-to-exceed values. Depending upon the site data 

availability, point-by-point site observations are compared with the estimated (or pre-established) BTVs 

and not-to-exceed values. If enough site and background data are available, two-sample hypotheses 

testing approaches are used to compare site concentrations with background concentrations levels. These 

statistical methods can also be used to compare contaminant concentrations of two site AOCs, surface and 

subsurface contaminant concentrations, or upgradient versus monitoring well contaminant concentrations. 

2 
ProUCL 4.0 Capabilities 

ProUCL 4.0 is an upgrade of ProUCL Version 3.0 (EPA, 2004). ProUCL 4.0 contains statistical methods 

to address various environmental issues for both full data sets without nondetects and for data sets with 

NDs (also known as left-censored data sets). 

ProUCL 4.0 contains: 

1. Rigorous parametric and nonparametric (including bootstrap methods) statistical methods 

(instead of simple ad hoc or substitution methods) that can be used on full data sets 

without nondetects and on data sets with below detection limit (BDL) or nondetect (ND) 

observations. 

2. State-of-the-art parametric and nonparametric UCL, UPL, and UTL computation 

methods. These methods can be used on full-uncensored data sets without nondetects and 

also on data sets with BDL observations. Some of the methods (e.g., Kaplan-Meier 

method, ROS methods) are applicable on left-censored data sets having multiple 

detection limits. The UCL and other upper limit computation methods cover a wide range 

of skewed data sets with and without the BDLs. 

3. Single sample (e.g., Student’s t-test, sign test, proportion test, Wilcoxon Singed Rank 

test) and two-sample (Student’s t-test, Wilcoxon-Mann-Whitney test, Gehan test, quantile 

test) parametric and nonparametric hypotheses testing approaches for data sets with and 

without ND observations. These hypothesis testing approaches can be used to: verify the 

attainment of cleanup standards, perform site versus background comparisons, and 

compare two or more AOCs, monitoring wells (MWs). 

4. The single sample hypotheses testing approaches are used to compare site mean, site 

median, site proportion, or a site percentile (e.g., 95th) to a compliance limit (action level, 

regularity limit). The hypotheses testing approaches can handle both full-uncensored data 

sets without nondetects, and left-censored data sets with nondetects. Simple two-sample 

hypotheses testing methods to compare two populations are available in ProUCL 4.0, 

such as two-sample t-tests, Wilcoxon-Mann-Whitney (WMW) Rank Sum test, quantile 

test, Gehan’s test, and dispersion test. Variations of hypothesis testing methods (e.g., 

Levene’s method to compare dispersions, generalized WRS test) are easily available in 

most commercial and freely available software packages (e.g., MINITAB, R). 

5. ProUCL 4.0 includes graphical methods (e.g., box plots, multiple Q-Q plots, histogram) 

to compare two or more populations. Additionally, ProUCL 4.0 can also be used to 

display a box plot of one population (e.g., site data) with compliance limits or upper 

limits (e.g., UPL) of other population (background area) superimposed on the same 

graph. This kind of graph provides a useful visual comparison of site data with a 

compliance limit or BTVs. Graphical displays of a data set (e.g., Q-Q plot) should be 

used to gain insight knowledge contained in a data set that may not otherwise be clear by 

looking at simple test statistics such as t-test, Dixon test statistic, or Shapiro-Wilk (S-W) 

test statistic. 

3 
6. ProUCL 4.0 can process multiple contaminants (variables) simultaneously and has the 

capability of processing data by groups. A valid group column should be included in the 

data file. 

7. ProUCL 4.0 provides a GOF test for data sets with nondetects. The user can create 

additional columns to store extrapolated (estimated) values for nondetects based upon 

normal ROS, gamma ROS, and lognormal ROS (robust ROS) methods. 

ProUCL Applications 

The methods incorporated in ProUCL 4.0 can be used on data sets with and without BDL and ND 

observations. Methods and recommendations as incorporated in ProUCL 4.0 are based upon the results 

and findings of the extensive simulation studies as summarized in Singh and Singh (2003), and Singh, 

Maichle, and Lee (EPA, 2006). It is anticipated that ProUCL 4.0 will serve as a companion software 

package for the following EPA documents: 

• Calculating Upper Confidence Limits for Exposure Point Concentrations at Hazardous 

Waste Sites (EPA, 2002a), and 

• Guidance for Comparing Background and Chemical Concentrations in Soil for CERCLA 

Sites (EPA, 2002b). 

Methods included in ProUCL 4.0 can be used in various other environmental applications including the 

verification of cleanup standards (EPA, 1989), and computation of upper limits needed in groundwater 

monitoring applications (EPA, 1992 and EPA, 2004). 

In 2002, EPA issued guidance for calculating the UCLs of the unknown population means for 

contaminant concentrations at hazardous waste sites. The ProUCL 3.0 software package (EPA, 2004) has 

served as a companion software package for the EPA (2002a) guidance document for calculating UCLs of 

mean contaminant concentrations at hazardous waste sites. ProUCL 3.0 has several parametric and 

nonparametric statistical methods that can be used to compute appropriate UCLs based upon fulluncensored data sets without any ND observations. ProUCL 4.0 retains the capabilities of ProUCL 3.0, 

including goodness-of-fit (GOF) and the UCL computation methods for data sets without any BDL 

observations. However, ProUCL 4.0 has the additional capability to perform GOF tests and computing 

UCLs and other upper limits based upon data sets with BDL observations. 

ProUCL 4.0 defines log-transform (log) as the natural logarithm (ln) to the base e. ProUCL 4.0 also 

computes the maximum likelihood estimates (MLEs) and the minimum variance unbiased estimates 

(MVUEs) of unknown population parameters of normal, lognormal, and gamma distributions. This, of 

course, depends upon the underlying data distribution. ProUCL 4.0 computes the (1 – α)100% UCLs of 

the unknown population mean, μ1, using 5 parametric and 10 nonparametric methods. It should be pointed 

out that ProUCL 4.0 computes the simple summary statistics for detected raw and log-transformed data 

for full data sets without NDs, as well as for data sets with BDL observations. It is noted that estimates of 

mean and sd for data sets with NDs based upon rigorous statistical methods (e.g., MLE, ROS, K-M 

methods) are note provided in the summary statistics. Those estimates and the associated upper limits for 

data sets with NDs are provided under the menu options: Background and UCL. 

It is emphasized that throughout this Technical Guide, and in the ProUCL 4.0 software, it is assumed that 

one is dealing with a single population. If multiple populations (e.g., background and site data mixed 

together) are present, it is recommended to first separate them out (e.g., using appropriate statistical 

4 
population partitioning techniques), and then compute appropriate respective 95% UCLs separately for 

each of the identified populations. Outliers, if any, should be identified and thoroughly investigated. 

ProUCL 4.0 provides two commonly used simple classical outlier identification procedures: 1) Dixon 

test, and 2) Rosner test. Outliers distort most parametric statistics (e.g., mean, UCLs, upper prediction 

limits (UPLs), test statistics) of interest. Moreover, it should be noted that even though outliers might 

have minimal influence on hypotheses testing statistics based upon ranks (e.g., WMW test), outliers do 

distort those nonparametric statistics (including bootstrap methods), which are based upon higher order 

statistics such as UPLs and UTLs. Decisions about the disposition (exclusion or inclusion) of outliers in a 

data set used to estimate the EPC terms or BTVs should be made by all parties involved (e.g., project 

team, EPA, local agency, potentially responsible party, etc.) in the decision making process. 

The presence of outlying observations also distorts statistics based upon bootstrap re-samples. The use of 

higher order values (quantiles) of the distorted statistics for the computation of the UCLs or UPLs based 

upon bootstrap t and Hall’s bootstrap methods may yield unstable and erratic UCL values. This is 

especially true for the upper limits providing higher confidence coefficients such as 95%, 97.5%, or 99%. 

Similar behavior of the bootstrap t UCL is observed for data sets having BDL observations. Therefore, the 

bootstrap t and Hall’s bootstrap methods should be used with caution. It is suggested that the user should 

examine various other UCL results and determine if the UCLs based upon the bootstrap t and Hall’s 

bootstrap methods represent reasonable and reliable UCL values of practical merit. If the results based 

upon these two bootstrap methods are much higher than the rest of methods, then this could be an 

indication of erratic behavior of those bootstrap UCL values, perhaps distorted by outlying observations. 

In case these two bootstrap methods yield erratic and inflated UCLs, the UCL of the mean should be 

computed using the adjusted or the approximate gamma UCL computation method for highly skewed 

gamma distributed data sets of small sizes. Alternatively, one may use a 97.5% or 99% Chebyshev UCL 

to estimate the mean of a highly skewed population. It should be noted that typically, a Chebyshev UCL 

may yield conservative and higher values of the UCLs than other methods available in ProUCL 4.0 This 

is especially true when data are moderately skewed and sample size is large. In such cases, when the 

sample size is large, one may want to use a 95% Chebyshev UCL or a Chebyshev UCL with lower 

confidence coefficient such as 92.5% or 90% as estimate of the population mean. 

ProUCL Methods 

ProUCL 4.0 provides 15 UCL computation methods for full data sets without any BDL observations; 5 

are parametric and 10 are nonparametric methods. The nonparametric methods do not depend upon any 

assumptions about the data distributions. The five parametric UCL computation methods are: 

1. Student’s t-UCL, 

2. Approximate gamma UCL using chi-square approximation, 

3. Adjusted gamma UCL (adjusted for level significance), 

4. Land’s H-UCL, and 

5. Chebyshev inequality-based UCL (using MVUEs of parameters of a lognormal 

distribution). 

5 
The 10 nonparametric methods are: 

1. The central limit theorem (CLT)-based UCL, 

2. Modified t-statistic (adjusted for skewness)-based UCL, 

3. Adjusted-CLT (adjusted for Skewness)-based UCL, 

4. Chebyshev inequality based-UCL (using sample mean and sample standard deviation), 

5. Jackknife method-based UCL, 

6. UCL based upon standard bootstrap, 

7. UCL based upon percentile bootstrap, 

8. UCL based upon bias-corrected accelerated (BCA) bootstrap, 

9. UCL based upon bootstrap t, and 

10. UCL based upon Hall’s bootstrap. 

Environmental scientists often encounter trace level concentrations of COPCs when evaluating sample 

analytical results. Those low level analytical results cannot be measured accurately, and therefore are 

typically reported as less than one or more DL values. However, the practitioners need to obtain reliable 

estimates of the population mean, μ1, and the population standard deviation, σ1, and upper limits including 

the UCL of the population mass (measure of central tendency) or mean, UPL, and UTL. Several methods 

are available and cited in the environmental literature (Helsel (2005), Singh and Nocerino (2002), Millard 

and Neerchal (2001)) that can be used to estimate the population mean and variance. However, till to date, 

no specific recommendations are available for the use of appropriate methods that can be used to compute 

upper limits (e.g., UCLs, UPLs) based upon data sets with BDL observations. Singh, Maichle, and Lee 

(EPA 2006) extensively studied the performance of several parametric and nonparametric UCL 

computation methods for data sets with BDL observations. Based upon their results and findings, several 

methods to compute upper limits (UCLs, UPLs, and UTLs) needed to estimate the EPC terms and BTVs 

have been incorporated in ProUCL 4.0. 

In 2002, EPA issued another Guidance for Comparing Background and Chemical Concentrations in Soil 

for CERCLA Sites (EPA, 2002b). This EPA (2002b) background guidance document is currently being 

revised to include statistical methods that can be used to estimate the BTVs and not-to-exceed values 

based upon data sets with and without the BDL observations. In background evaluation studies, BTVs, 

compliance limits, or not-to-exceed values often need to be estimated based upon defensible background 

data sets. The estimated BTVs or not-to-exceed values are then used for screening the COPCs, to identify 

the site AOCs or hot spots, and also to determine if the site concentrations (perhaps after a remediation 

activity) are comparable to background concentrations, or are approaching the background level 

concentrations. Individual point-by-point site observations (composite samples preferred) are sometimes 

compared with those not-to-exceed values or BTVs. It should be pointed out that in practice, it is 

preferred to use hypotheses testing approaches to compare site versus background concentrations 

provided enough (e.g., at least 8-10 detected observations from each of the two populations) site and 

background data are available. Chapter 1 provides practical guidance on the minimum sample size 

6 
requirements to estimate and use the BTVs, single and two-sample hypotheses testing approaches to 

perform background evaluations and background versus site comparisons. Chapter 1 also briefly 

discusses the differences in the definitions and uses of the various upper limits as incorporated in ProUCL 

4.0. Detailed discussion of the various methods to estimate the BTVs and other not-to-exceed values for 

full-uncensored data sets (Chapter 5) without any nondetect values and for left-censored data sets 

(Chapter 6) with nondetect values are given in the revised background guidance document. 

ProUCL 4.0 includes statistical methods to compute UCLs of the mean, upper limits to estimate the 

BTVs, other not-to-exceed values, and compliance limits based upon data sets with one or more detection 

limits. The use of appropriate statistical methods and limits for exposure and risk assessment, and site 

versus background comparisons, is based upon several factors: 

1. Objective of the study; 

2. Environmental medium (e.g., soil, groundwater, sediment, air) of concern; 

3. Quantity and quality of the available data; 

4. Estimation of a not-to-exceed value or of a mean contaminant concentration; 

5. Pre-established or unknown cleanup standards and BTVs; and 

6. Sampling distributions (parametric or nonparametric) of the concentration data sets 

collected from the site and background areas under investigation. 

In background versus site comparison studies, the population parameters of interest are typically 

represented by upper threshold limits (e.g., upper percentiles, upper confidence limits of an upper 

percentile, upper prediction limit) of the background data distribution. It should be noted that the upper 

threshold values are estimated and represented by upper percentiles and other values from the upper tail 

of the background data distribution. These background upper threshold values do not represent measures 

of central tendency such as the mean, the median, or their upper confidence limits. These environmental 

parameters may include: 

• Preliminary remediation goals (PRGs), compliance limits, 

• Soil screening levels (SSLs), 

• Risk-based cleanup (RBC) standards, 

• BTVs, compliance limits, or not-to-exceed values, and 

• Maximum concentration limit (MCL) or alternative concentration limit (ACL) used in 

Groundwater applications. 

When the environmental parameters listed above are not known or pre-established, appropriate upper 

statistical limits are used to estimate those parameters. The UPL, UTL, and upper percentiles are typically 

used to estimate the BTVs, not-to-exceed values, and other parameters listed above. Depending upon the 

availability of site data, point-by-point site observations are compared with the estimated (or preestablished) BTVs and not-to-exceed values. If enough site and background data are available, twosample hypotheses testing approaches (preferred method to compare two populations) are used to 

compare site concentrations with background concentrations levels. The hypotheses testing methods can 

also be used to compare contaminant concentrations of two site AOCs, surface and subsurface 

contaminant concentrations, or upgradient versus monitoring well contaminant concentrations. 

7 
Background versus Site Comparison Evaluations 

The following statistical limits have been incorporated in ProUCL 4.0 to assist in background versus site 

comparison evaluations: 

Parametric Limits for Full-Uncensored Data Sets without Nondetect Observations 

• UPL for a single observation (Normal, Lognormal) not belonging to the original data set 

• UPL for next k (k is user specified) or k future observations (Normal, Lognormal) 

• UTL, an upper confidence limit of a percentile (Normal, Lognormal) 

• Upper percentiles (Normal, Lognormal, and Gamma) 

Nonparametric Limits for Full-Uncensored Data Sets without Nondetect Observations 

Nonparametric limits are typically based upon order statistics of a data set such as a background or a 

reference data set. Depending upon the size of the data set, higher order statistics (maximum, second 

largest, third largest, and so on) are used as these upper limits (e.g., UPLs, UTLs). The details of these 

methods with sample size requirements can be found in Chapter 5 of the revised Guidance for Comparing 

Background and Chemical Concentrations in Soil for CERCLA Sites (EPA, 2002b). It should be, noted 

that the following statistics might get distorted by the presence of outliers (if any) in the data set under 

study. 

• UPL for a single observation not belonging to the original data set 

• UTL, an upper confidence limit of a percentile 

• Upper percentiles 

• Upper limit based upon interquartile range (IQR) 

• Upper limits based upon bootstrap methods 

For data sets with BDL observations, the following parametric and nonparametric methods to compute 

the upper limits were studied and evaluated by Singh, Maichle, and Lee (EPA, 2006) via Monte Carlo 

Simulation Experiments. Depending upon the performances of those methods, only some of the methods 

have been incorporated in ProUCL 4.0. Methods (e.g., Delta method, DL method, uniform (0, DL) 

generation method) not included in ProUCL 4.0 do not perform well in comparison with other methods. 

Note: When the percentage of NDs in a data set is high (e.g., > 40%-50%), especially when multiple 

detection limits might be present, it is hard to reliably perform GOF tests (to determine data distribution) 

on those data sets with many NDs. The uncertainty associated with those GOF tests will be high, 

especially when the data sets are of small sizes (< 10-20). It should also be noted that the parametric 

MLE methods (e.g., for normal and lognormal distributions) often yield unstable estimates of mean and 

sd. This is especially true when the number of nondetects exceeds 40%-50%. In such situations, it is 

preferable to use nonparametric (e.g., KM method) methods to compute statistics of interest such as 

UCLs, UPLs, and UTLs. Nonparametric methods do not require any distributional assumptions about the 

data sets under investigation. Singh, Maichle, and Lee (EPA, 2006) also concluded that the performance 

of the KM estimation method is better (in terms of coverage probabilities) than various other parametric 

estimation (e.g., MLE, EM, ROS) methods. 

8 
Parametric Methods to Compute Upper Limits for Data Sets with Nondetect Observations 

• Simple substitution (proxy) methods (0, DL/2, DL) 

• MLE method, often known as Cohen’s MLE method – single detection limit 

• Restricted MLE method – single detection limit – not in ProUCL 4.0 

• Expectation Maximization (EM) method – single detection limit – not in ProUCL 4.0 

• EPA Delta log method – single detection limit – not in ProUCL 4.0 

• Regression method on detected data and using slope and intercept of the OLS regression 

line as estimates of standard deviation, sd, and mean (not a recommended method) 

• Robust ROS (regression on order statistics) on log-transformed data – nondetects 

extrapolated (estimated) using robust ROS; mean, sd, UCLs, and other statistics 

computed using the detected and extrapolated data in original scale – multiple detection 

limits 

• Normal ROS – nondetects extrapolated (estimated) using normal distribution, mean, sd, 

UCLs, and other statistics computed using the detected and extrapolated data – multiple 

detection limits. 

• It is noted that the estimated NDs often become negative and even larger than the 

detection limits (not a recommended method) 

• Gamma ROS – nondetects extrapolated (estimated) using gamma distribution, mean, sd, 

UCLs, and other statistics computed using the detected and extrapolated data – multiple 

detection limits 

Nonparametric Methods to Compute Upper Limits for Data Sets with Nondetect Observations 

• Bootstrap Methods 

o Percentile Bootstrap on robust ROS 

o Percentile Bootstrap 

o BCA Bootstrap 

o Bootstrap t 

• Jackknife Method 

o Jackknife on robust ROS 

• Kaplan-Meier (KM) Method 

o Bootstrap (percentile, BCA) using KM estimates 

o Jackknife using KM estimates 

o Chebyshev Method using KM estimates 

• Winsorization Method 

For uncensored full data sets without any NDs, the performance (in terms of coverage for the mean) of 

the various UCL computation methods was evaluated by Singh and Singh (2003). The performance of the 

parametric and nonparametric UCL methods based upon data sets with nondetect observations was 

studied by Singh, Maichle, and Lee (EPA 2006). Several of the methods listed above have been 

incorporated in ProUCL 4.0 to compute the estimates of EPC terms (95% UCL), and of BTVs (UPLs, 

UTLs, upper percentiles). Methods that did not perform well (e.g., poor coverage or unrealistically large 

values, infeasible and biased estimates) are not included in ProUCL 4.0. Methods not incorporated in 

9 
ProUCL 4.0 are: EPA Delta Log method, Restricted MLE method, and EM method, substitution method 

(0, and DL), and Regression method. 

Note: It should be noted that for data sets with NDs, the DL/2 substitution method has been incorporated 

in ProUCL 4.0 only for historical reasons and also for its current default use. It is well known that the 

DL/2 method (with NDs replaced by DL/2) does not perform well (e.g., Singh, Maichle, and Lee (EPA 

2006)) even when the percentage of NDs is only 5%-10%. It is strongly suggested to avoid the use of 

DL/2 method for estimation and hypothesis testing approaches used in various environmental 

applications. Also, when the % of NDs becomes high (e.g., > 40%-50%), it is suggested to avoid the use 

of parametric MLE methods. For data sets with high percentage of NDs (e.g., > 40%), the distributional 

assumptions needed to use parametric methods are hard to verify; and those parametric MLE methods 

may yield unstable results. 

It should also be noted that even though the lognormal distribution and some statistics based upon 

lognormal assumption (e.g., Robust ROS, DL/2 method) are available in ProUCL 4.0, ProUCL 4.0 does 

not compute MLEs of mean and sd based upon a lognormal distribution. The main reason is that the 

estimates need to be computed in the original scale via back-transformation (Shaarawi, 1989, Singh, 

Maichle, and Lee (EPA 2006)). Those back-transformed estimates often suffer from an unknown amount 

of significant bias. Hence, it is also suggested to avoid the use of a lognormal distribution to compute 

MLEs of mean and sd, and associated upper limits, especially UCLs based upon those MLEs obtained 

using a lognormal distribution. 

ProUCL 4.0 recommends the use of an appropriate UCL to estimate the EPC terms. It is desirable that the 

user consults with the project team and experts familiar with the site before using those recommendations. 

Furthermore, there does not seem to be a general agreement about the use of an upper limit (e.g., UPL, 

percentile, or UTL) to estimate not-to-exceed values or BTVs to be used for screening of the COPCs and 

in site versus background comparison studies. ProUCL 4.0 can compute both parametric and 

nonparametric upper percentiles, UPLs, and UTLs for uncensored and censored data sets. However, no 

specific recommendations have been made regarding the use of UPLs, UTLs, or upper percentiles to 

estimate the BTVs, compliance limits, and other related background or reference parameters. However, 

the developers of ProUCL 4.0 prefer the use of UPLs or upper percentiles to estimate the background 

population parameters (e.g., BTVs, not-to-exceed values) that may be needed to perform point-by-point 

site versus background comparisons. 

The standard bootstrap and the percentile bootstrap UCL computation methods do not perform well (do 

not provide adequate coverage to population mean) for skewed data sets. For skewed distributions, the 

bootstrap t and Hall’s bootstrap (meant to adjust for skewness) methods do perform better (in terms of 

coverage for the population mean) than the other bootstrap methods. However, it has been noted (e.g., 

Efron and Tibshirani (1993) and Singh, Singh, and Iaci (2002b)) that these two bootstrap methods 

sometimes yield erratic and inflated UCL values (orders of magnitude higher than the other UCLs). This 

may occur when outliers are present in a data set. Similar behavior of the bootstrap t UCL is observed 

based upon data sets with NDs. Therefore, whenever applicable, ProUCL 4.0 provides cautionary 

statements regarding the use of bootstrap methods. 

ProUCL 4.0 provides several state-of-the-art parametric and nonparametric UCL, UPL, and UTL 

computation methods that can be used on uncensored data sets (full data sets) and on data sets with BDL 

observations. Some of the methods (e.g., Kaplan-Meier method, ROS methods) incorporated in ProUCL 

4.0 are applicable on left-censored data sets having multiple detection limits. The UCLs and other upper 

10 
limits computation methods in ProUCL 4.0 cover a wide range of skewed data distributions with and 

without the BDLs arising from the environmental applications. 

ProUCL 4.0 also has parametric and nonparametric single and two-sample hypotheses testing approaches 

required to: compare site location (e.g., mean, median) to a specified cleanup standard; perform site 

versus background comparisons; or compare of two or more AOCs. These hypotheses testing methods 

can handle both full (uncensored data sets without NDs) and left-censored (with nondetects) data sets. 

Specifically, two-sample tests such as t-test, Wilcoxon-Mann-Whitney (WMW) Rank Sum test, quantile 

test, and Gehan’s test are available in ProUCL 4.0 to compare concentrations of two populations. 

Single sample parametric (Student’s t-test) and nonparametric (sign test, Wilcoxon Signed Rank (WSR) 

test, tests for proportions and percentiles) hypotheses testing approaches are also available in ProUCL 4.0. 

The single sample hypotheses tests are useful when the environmental parameters such as the clean 

standard, action level, or compliance limits are known, and the objective is to compare site concentrations 

with those known threshold values. Specifically, a t-test (or a sign test) may be used to verify the 

attainment of cleanup levels at an AOC after a remediation activity; and a test for proportion may be used 

to verify if the proportion of exceedances of an action level (or a compliance limit) by sample 

concentrations collected from the AOC (or a MW) exceeds a certain specified proportion (e.g., 1%, 5%, 

10%). As mentioned before, ProUCL 4.0 can perform these hypotheses on data sets with and without 

nondetect observations. 

Note: It should be noted that as cited in the literature, some of the hypotheses testing approaches (e.g., 

nonparametric two-sample WMW) deal with the single detection limit scenario. If multiple detection 

limits are present, all NDs below the largest detection limit need to be considered as NDs (Gilbert, 1987, 

Helsel, 2005). This in turn may reduce the power and increase uncertainty associated with test. As 

mentioned before, it is always desirable to supplement the test statistics and test conclusions with 

graphical displays such as the multiple Q-Q plots and side-by-side box plots. ProUCL 4.0 can graph box 

plots and Q-Q plots for data sets with nondetect observations. Gehan test as available in ProUCL 4.0 

should be used in case multiple detection limits are present. ProUCL 4.0 can draw Q-Q plots and box 

plots for data sets with and without nondetect observations. 

It should be pointed out that when using two-sample hypotheses approaches (WMW test, Gehan test, and 

quantile test) on data sets with NDs, both samples and variables (e.g., site-As, Back-As) should be 

specified as having nondetects. This means, a ND column (0= ND, and 1 = detect) should be provided for 

each variable (here D_site-As, and D_Back-As) to be used in this comparison. If a variable (e.g., site-As) 

does not have any nondetects, still a column with label D_site-As should be included in the data set with 

all entries = 1 (detected values). 

Moreover, in single sample hypotheses tests (e.g., sign test, proportion test) used to compare site 

mean/median concentration level with a cleanup standard, Cs, or compliance limit (e.g., proportion test), 

all NDs (if any) should lie below the cleanup standard, Cs. 

The differences between these tests should be noted and understood. Specifically, a t-test or a Wilcoxon 

Signed Rank (WSR) test are used to compare the measures of location and central tendencies (e.g., mean, 

median) of a site area (e.g., AOC) to a cleanup standard, Cs or action level also representing a measure of 

central tendency (e.g., mean, median); whereas, a proportion test compares if the proportion of site 

observations from an AOC exceeding a compliance limit (CL) exceeds a specified proportion, P0 (e.g., 

5%, 10%). The percentile test compares a specified percentile (e.g., 95th) of the site data to a pre-specified 

upper threshold (e.g., reporting limit, action level). All of these tests have been incorporated in ProUCL 

11 
4.0. Most of the single sample and two-sample hypotheses tests also report associated p-values. For some 

of the hypotheses tests (e.g., WMW test, WSR test, proportion test), large sample approximate p-values 

are computed using continuity correction factors. 

Graphical Capabilities 

ProUCL 4.0 has useful exploratory graphical methods that may be used to visually compare the 

concentrations of: 

1. A site area of concern (AOC) with an action level. This can be done using a box plot of 

site data with action level superimposed on that graph, 

2. Two or more populations, including site versus background populations, surface versus 

subsurface concentrations, and 

3. Two or more AOCs. 

The graphical methods include double and multiple quantile-quantile (Q-Q) plots, side-by-side box plots, 

and histograms. Whenever possible, it is desirable to supplement statistical test results and statistics with 

visual graphical displays of data sets. There is no substitute for graphical displays of a data set as the 

visual displays often provide useful information about a data set, which cannot be revealed by simple test 

statistics such as t-test, SW test, Rosner test, WMW test. For example, in addition to providing 

information about the data distribution, a normal Q-Q plot can also help identify outliers and multiple 

populations that might be present in a data set. This kind of information cannot be revealed by simple test 

statistics such as a Shapiro-Wilk (SW) test or Rosner’s outlier test statistic. Specifically, the SW test may 

lead to the conclusion that a mixture data set (representing two or more populations) can be modeled by a 

normal (or lognormal) distribution, whereas the occurrence of obvious breaks and jumps in the associated 

Q-Q plot may suggest the presence of multiple populations in the mixture data set. It is suggested that the 

user should use exploratory tools to gain necessary insight into a data set and the underlying assumptions 

(e.g., distributional, single population) that may not be revealed by simple test statistics. 

Note: On a Q-Q plot, observations well separated from the majority of the data may represent potential 

outliers, and obvious jumps and breaks of significant magnitude may suggest the presence of observations 

from multiple populations in the data set. 

The analyses of data categorized by a group ID variable such as: 1) Surface vs. Subsurface; 

2) AOC1 vs. AOC2; 3) Site vs. Background; and 4) Upgradient vs. Downgradient monitoring wells are 

quite common in many environmental applications. ProUCL 4.0 offers this option for data sets with and 

without nondetects. The Group Option provides a powerful tool to perform various statistical tests and 

methods (including graphical displays) separately for each of the group (samples from different 

populations) that may be present in a data set. For an example, the same data set may consist of samples 

from the various groups or populations representing site, background, two or more AOCs, surface, 

subsurface, monitoring wells. The graphical displays (e.g., box plots, Q-Q plots) and statistics 

(computations of background statistics, UCLs, hypotheses testing approaches) of interest can be 

computed separately for each group by using this option. 

12 
Chapter 1 

Guidance on the Use of Statistical Methods as Incorporated in 

ProUCL 4.0 & Associated Minimum Sample Size Requirements 

This chapter describes the differences between the various statistical limits (e.g., UCLs, UPLs, UTLs) 

often used to estimate the environmental parameters of interest including exposure point concentration 

(EPC) terms and background threshold values (BTVs). Some suggestions about the minimum sample size 

requirements needed to use statistical inferential methods to estimate the environmental parameters: EPC 

terms, BTVs, and not-to-exceed values, and to compare site data with background data or with some preestablished reference limits (e.g., preliminary remediation goals (PRGs), action levels, compliance limits) 

have also been provided. It is noted that, several EPA guidance documents (e.g., EPA, 1997, 2002a, 2006) 

discuss in details about data quality objectives (DQOs) and sample size determinations based upon those 

DQOs needed for the various statistical methods used in environmental applications. Also, appropriate 

sample collection methods (e.g., instruments, sample weights, discrete or composite, analytical methods) 

depend upon the medium (e.g., soil, sediment, water) under consideration. For an example, Gerlach and 

Nocerino (EPA, 2003) describe optimal soil sample (based upon Gy theory) collection methods. 

Therefore, the topics of sample size determination based upon DQOs, data validation, and appropriate 

sample collection methods for the various environmental media are not considered in ProUCL 4.0 and the 

associated technical and technical guides. It is assumed that data sets to be used in ProUCL are of good 

quality, and whenever possible have been obtained using the guidance provided in various EPA (2003, 

2006) documents. It is users’ responsibility to assure that adequate amount of good quality data have been 

collected. 

Note: Here, emphasis is given on the practical applicability and appropriate use of statistical methods 

needed to address statistical issues arising in risk management, background versus site evaluation 

studies, and various other environmental applications. Specifically, guidance on minimum sample size 

requirements as provided in this chapter is useful when data have already been collected, or it is not 

possible (e.g., due to resource limitations) to collect the number of samples obtained using DQO 

processes as described in EPA (2006). 

Decisions based upon statistics obtained using data sets of small sizes (e.g., 4 to 6 detected observations) 

cannot be considered reliable enough to make a remediation decision that affects human health and the 

environment. For an example, a background data set of size 4 to 6 is not large enough to characterize 

background population, to compute BTV values, or to perform background versus site comparisons. In 

order to perform reliable and meaningful statistical inference (estimation and hypothesis testing), one 

should determine the sample sizes that need to be collected from the populations under investigation 

using appropriate DQO processes and decision error rates (EPA, 2006). However, in some cases, it may 

not be possible (e.g., resource constraints) to collect the same number of samples recommended by the 

DQO process. In order to address such cases, some minimum sample size requirements for background 

and site data sets are described in this chapter. 

The use of an appropriate statistical method depends upon the environmental parameter(s) being 

estimated or compared. The measures of central tendency (e.g., means, medians, or their upper confidence 

limits (UCLs)) are often used to compare site mean concentrations (e.g., after remediation activity) with a 

cleanup standard, Cs, representing some central tendency measure of a reference area or some other 

known threshold representing a measure of central tendency. The upper threshold values, such as the 

compliance limits (e.g., alternative concentration limit (ACL), maximum concentration limit (MCL)), or 

13 
not-to-exceed values, are used when individual point-by-point observations are compared with those notto-exceed values or some other compliance limit. It should be noted that depending upon whether the 

environmental parameters (e.g., BTVs, not-to-exceed value, EPC term, or cleanup standards) are known 

or unknown, different statistical methods with different data requirements are needed to compare site 

concentrations with pre-established (known) or estimated (unknown) standards and BTVs. 

ProUCL 4.0 has been developed to address issues arising in exposure assessment, risk- assessment, and 

background versus site comparison applications. Several upper limits, and single- and two-sample 

hypotheses testing approaches, for both full-uncensored and left-censored data sets, are available in 

ProUCL 4.0. The details of the statistical and graphical methods included in ProUCL 4.0 can be found in 

the ProUCL Technical guidance. In order to make sure that the methods in ProUCL 4.0 are properly used, 

this chapter provides guidance on: 

1. analysis of site and background areas and data sets, 

2. collection of discrete or composite samples, 

3. appropriate use of the various upper limits, 

4. guidance regarding minimum sample sizes, 

5. point-by-point comparison of site observations with BTVs, 

6. use of hypotheses testing approaches, 

7. using small data sample sets, 

8. use of maximum detected value, and 

9. discussion of ProUCL usage for special cases. 

1.1 Background Data Sets 

The project team familiar with the site should identify and chose a background area. Depending upon the 

site activities and the pollutants, the background area can be site-specific or a general reference area. An 

appropriate random sample of independent observations should be collected from the background area. A 

defensible background data set should represent a “single” background population (e.g., representing 

pristine site conditions before any of the industrial site activities) free of contaminating observations such 

as outliers. In a background data set, outliers may represent potentially contaminated observations from 

impacted site areas under study or possibly from other polluted site(s). This scenario is common when 

background samples are obtained from the various onsite areas (e.g., large federal facilities). Outlying 

observations should not be included in the estimation (or hypotheses testing procedures) of the BTVs. 

The presence of outliers in the background data set will yield distorted estimates of the BTVs and 

hypothesis testing statistics. The proper disposition of outliers to include or not include them in the data 

set should be decided by the project team. 

Decisions based upon distorted statistics can be incorrect, misleading, and expensive. It should be noted 

that the objective is to compute background statistics based upon the majority of the data set representing 

the dominant background population, and not to accommodate a few low probability outliers that may 

14 
also be present in the background data set. A couple of simple classical outlier tests (Dixon and Rosner 

tests) are available in ProUCL 4.0. Since these classical tests suffer from masking effects (e.g., some 

extreme outliers may mask the occurrence of other intermediate outliers), it is suggested that these 

classical outlier tests should always be supplemented with graphical displays such as a box plot or a Q-Q 

plot. The use of robust and resistant outlier identification procedures (Singh and Nocerino, 1995, 

Rousseeuw and Leroy, 1987) is recommended when multiple outliers may be present in a data set. Those 

methods are beyond the scope of ProUCL 4.0. However, several robust outlier identification and 

estimation are available in Scout (EPA, 2000), which is currently under revision and upgrade. 

An appropriate background data set of a reasonable size (preferably computed using DQO processes) is 

needed to characterize a background area including computation of upper limits (e.g., estimates of BTVs, 

not-to-exceed values) based upon background data sets and also to compare site and background data sets 

using hypotheses testing approaches. As mentioned before, a small background data set of size 4 to 6 is 

not large enough to compute BTVs or to perform background versus site comparisons. At the minimum, a 

background sample should have at least 8 to 10 (more observations are preferable) detected observations 

to estimate BTVs or to use hypotheses testing approaches. 

1.2 Site Data Sets 

A defensible data set from a site population (e.g., AOC, EA, RU, group of monitoring wells) should be 

representative of the site area under investigation. Depending upon the site areas under investigation, 

different soil depths and soil types may be considered as representing different statistical populations. In 

such cases, background versus site comparisons may have to be conducted separately for each of those 

site sub-populations (e.g., surface and sub-surface layers of an AOC, clay and sandy site areas). These 

issues, such as comparing depths and soil types, should also be considered in a planning and sampling 

design before starting to collect samples from the various site areas under investigation. Specifically, the 

availability of an adequate amount of representative site data is required from each of those site subpopulations defined by sample depths, soil types, and the various other characteristics. For detailed 

guidance on soil sample collections, the reader is referred to Gerlach and Nocerino (EPA (2003)). 

The site data collection requirements depend upon the objective of the study. Specifically, in background 

versus site comparisons, site data are needed to perform: 

• Individual point-by-point site observation comparisons with pre-established or estimated 

BTVs, PRGs, cleanup standards, and not-to-exceed-values. Typically, this approach is 

used when only a small number (e.g., < 4 to 6) of detected site observations (preferably 

based upon composite samples) are available which need to be compared with BTVs and 

not-to-exceed values. Some applications of the point-by-point site observation 

comparisons are described later in this chapter. 

• Single sample hypotheses tests to compare site data with pre-established cleanup 

standards, Cs (e.g., representing a measure of central tendency); or with BTVs and not-toexceed values (used for tests for proportions and percentiles). The hypotheses testing 

approaches are used when enough site data are available. Specifically, when at least 8 to 

10 detected (more are desirable) site observations are available, it is preferable to use 

hypotheses testing approaches to compare site observations with specified threshold 

values. The use of hypotheses testing approaches can control the two types (Type 1 and 

Type 2) of error rates more efficiently than the point-by-point individual observation 

comparisons. This is especially true as the number of point-by-point comparisons 

15 
increases. This issue is illustrated by the following table summarizing the probabilities of 

exceedances (false positive error rate) of the background threshold value (e.g., 95th 

percentile) by site observations, even when the site and background populations have 

comparable distributions. The probabilities of these chance exceedances increase as the 

sample size increases.

 Sample Size Probability of Exceedance 

1 0.05 

2 0.10 

5 0.23 

8 0.34 

10 0.40 

12 0.46 

64 0.96 

• Two-sample hypotheses testing to compare site data distribution with background data 

distribution to determine if the site concentrations are comparable to background 

concentrations. Adequate amount of data need to be made available from the site as well 

as the background populations. It is preferable to collect at least 8 to 10 detected 

observations from each of the population under comparison. 

1.3 Discrete Samples or Composite Samples? 

In a data set (background or site), collected samples should be either all discrete or all composite. In 

general, both discrete and composite site samples may be used for individual point-by-point site 

comparisons with a threshold value, and for single and two-sample hypotheses testing applications. 

• If possible, the use of composite site samples is preferred when comparing individual 

point-by-point site observations from an area (e.g., area of concern (AOC), remediation 

unit (RU), exposure area (EA)) with some pre-established or estimated BTV, compliance 

limit (CL), or some other not-to-exceed value. This comparison approach is useful when 

few (< 4 to 6) detected site observations are compared with a pre-established or estimated 

BTV or some other not-to-exceed threshold. 

• When using a single sample hypothesis testing approach, site data can be obtained by 

collecting all discrete or all composite samples. The hypothesis testing approach is used 

when many (e.g., exceeding 8 to 10) detected site observations are available. Details of 

the single sample hypothesis approaches are widely available in EPA documents (1989, 

1997, and 2006). Some of those single sample hypotheses testing procedures are also 

available in ProUCL 4.0. 

• If a two-sample hypotheses testing approach is used to perform site versus background 

comparisons, then samples from both of the populations should be either all discrete 

samples, or all composite samples. The two-sample hypothesis testing approach is used 

when many (e.g., exceeding 8 to 10) site, as well as background, observations are 

available. For better and more accurate results with higher statistical power, the 

availability of more observations (e.g., exceeding 10-15) from each of the two 

populations is desirable, perhaps based upon an appropriate DQO process, as described in 

an EPA guidance document (2006). 

16 
1.4 Upper Limits and Their Use 

The appropriate computation and use of statistical limits depend upon their applications and the 

parameters (e.g., EPC term, not-to-exceed value) they are supposed to be estimating. Depending upon the 

objective of the study, a pre-specified cleanup standard, Cs, or a risk-based cleanup (RBC) can be viewed 

as to represent: 1) as average contaminant concentration; or 2) a not-to-exceed upper threshold value. 

These two threshold values, an average value, μ0, and a not-to-exceed value, A0, represent two 

significantly different parameters, and different statistical methods and limits are used to compare the site 

data with these two different parameters or threshold values. Statistical limits, such as an upper 

confidence limit (UCL) of the population mean, an upper prediction limit (UPL) for an independently 

obtained “single” observation, or independently obtained k observations (also called future k 

observations, next k observations, or k different observations), upper percentiles, and upper tolerance 

limits (UTLs), are often used to estimate the environmental parameters, including the EPC terms, 

compliance limits (e.g., ACL, MCL), BTVs, and other not-to-exceed values. Here, UTL95%-95% 

represents a 95% confidence limit of the 95th percentile of the distribution of the contaminant under study. 

It is important to understand and note the differences between the uses and numerical values of these 

statistical limits so that they can be properly used. Specifically, the differences between UCLs and UPLs 

(or upper percentiles), and UCLs and UTLs should be clearly understood and acknowledged. A UCL with 

a 95% confidence limit (UCL95) of the mean represents an estimate of the population mean (measure of 

the central tendency of a data distribution), whereas a UPL95, a UTL95%-95%, and an upper 95th 

percentile represent estimates of a threshold value in the upper tail of the data distribution. Therefore, a 

UCL95 should represent a smaller number than an upper percentile or an upper prediction limit. Also, 

since a UTL 95%-95% represents a 95% UCL of the upper 95th percentile, a UTL should be ≥ the 

corresponding UPL95 and the 95th upper percentile. Typically, it is expected that the numerical values of 

these limits should follow the order given as follows: 

Sample Mean ≤ UCL95 of Mean ≤ Upper 95th Percentile ≤ UPL95 of a Single Observation ≤

UTL95%-95%

 It should also be pointed out that as the sample size increases, a UCL95 of the mean approaches 

(converges to) the population mean, and a UPL95 approaches the 95th percentile. The differences among 

the various upper limits are further illustrated in Example 1-1 below. It should be noted that, in some 

cases, these limits might not follow the natural order described above. This is especially true when the 

upper limits are computed based upon a lognormal distribution (Singh, Singh, and Engelhardt, 1997). It is 

well known that a lognormal distribution based H-UCL95 (Land’s UCL95) often yields unstable and 

impractically large UCL values. An H-UCL95 often becomes larger than UPL95 and even larger than a 

UTL 95%-95%. This is especially true when dealing with skewed data sets of smaller sizes. Moreover, it 

should also be noted that in some cases, a H-UCL95 becomes smaller than the sample mean, especially 

when the data are mildly skewed to moderately skewed, and the sample size is large (e.g., > 50, 100). 

Some of these issues, related to a lognormal distribution and H-UCL95 based upon Land’s (1975) statistic 

are discussed in Chapter 3 of the revised background document for CERCLA sites. 

1.4.1 Example 1-1 

Consider a simple site-specific background data set associated with a Superfund site. The data set (given 

in Appendix 5 of the revised Guidance for Comparing Background and Chemical Concentrations in Soil 

for CERCLA Sites (EPA, 2002b)) has several inorganic contaminants of potential concern, including 

aluminum, arsenic, chromium, iron, and lead. It is noted that iron concentrations follow a normal 

17 
distribution. Some upper limits for the iron data set are summarized as follows. It is noted that the various 

upper limits do follow the order as described above. 

Table 1-1. Computation of Upper Limits for Iron (Normally Distributed) 

Mean Median Min Max UCL95 

UPL95 for a 

Single 

Observation 

UPL95 for 4 

Observations 

UTL95/95 95% Upper 

Percentile 

9618 9615 3060 18700 11478 18145 21618 21149 17534 

A 95% UCL (UCL95) of the mean is the most commonly used limit in environmental applications. For an 

example, a 95% UCL of mean is used as an estimate of the EPC. A UCL95 should not be used to estimate 

a background threshold value (a value in the upper tail of the background data distribution) to be 

compared with individual site observations. There are many instances in background evaluations and 

background versus site comparison studies, when it is not appropriate to use a 95% UCL. Specifically, 

when point-by-point site observations are to be compared with a BTV, then that BTV should be estimated 

(or represented) by a limit from the upper tail of the reference set (background) data distribution. 

A brief discussion about the differences between the applications and uses of the various statistical limits 

is provided below. This will assist a typical user in determining which upper limit (e.g., UCL95 or 

UPL95) to use to estimate the parameter of interest (e.g., EPC or BTV). 

• A UCL represents an average value that should be compared with a threshold value also 

representing an average value (pre-established or estimated), such as a mean cleanup 

standard, Cs. For an example, a site 95% UCL exceeding a cleanup value, Cs, may lead to 

the conclusion that the cleanup level, Cs, has not been attained by the site area. It should 

be noted that UCLs of means are typically computed based upon the site data set. 

• When site averages (and not individual site observations) are compared with a threshold 

value (pre-determined or estimated), such as a PRG or a RBC, or with some other 

cleanup standard, Cs, then that threshold should represent an average value, and not a notto-exceed threshold value for individual observation comparisons. 

• A UCL represents a “collective” measure of central tendency, and it is not appropriate to 

compare individual site observations with a UCL. Depending upon data availability, 

single or two-sample hypotheses testing approaches are used to compare site averages: 

with a specified or pre-established cleanup standard (single sample hypothesis), or with 

the background population averages (two-sample hypothesis). 

• A UPL, an upper percentile, or an UTL represents an upper limit to be used for point-bypoint individual site observation comparisons. UPLs and UTLs are computed based upon 

background data sets, and individual site observations are compared with those limits. A 

site observation for a contaminant exceeding a background UTL or UPL may lead to the 

conclusion that the contaminant is a contaminant of potential concern (COPC) to be 

included in further risk evaluation and risk management studies. 

• When individual point-by-point site observations are compared with a threshold value 

(pre-determined or estimated) of a background population or some other threshold and 

compliance limit value, such as a PRG, MCL, or ACL, then that threshold value should 

represent a not-to-exceed value. Such BTVs or not-to-exceed values are often estimated 

18 
by a 95% UPL, UTL95%-95%, or by an upper percentile. ProUCL 4.0 can be used to 

compute any of these upper limits based upon uncensored data sets as well as data sets 

with nondetect values. 

• As the sample size increases, a UCL approaches the sample mean, and a UPL95 

approaches the corresponding 95th upper percentile. 

• It is pointed out that the developers of ProUCL 4.0 prefer the use of a 95% UPL (UPL95) 

as an estimate of BTV or a not-to-exceed value. As mentioned before, the option of 

comparing individual site observations with a BTV (specified or estimated) should be 

used when few (< 4 to 6) detected site observations (preferably composite values) are to 

be compared with a BTV. 

• When enough (e.g., > 8 to 10) detected site observations are available, it is preferred to 

use hypotheses testing approaches. Specifically, single sample hypotheses testing 

(comparing site to a specified threshold) approaches should be used to perform site 

versus a known threshold comparison; and two-sample hypotheses testing (provided 

enough background data are also available) approaches should be used to perform site 

versus background comparison. Several parametric and nonparametric single and twosample hypotheses testing approaches are available in ProUCL 4.0. 

It is re-emphasized that only averages should be compared with the averages or UCLs, and individual site 

observations should be compared with UPLs, upper percentiles, or UTLs. For an example, the comparison 

of a 95% UCL of one population (e.g., site) with a 90% or 95% upper percentile of another population 

(e.g., background) cannot be considered fair and reasonable as these limits (e.g., UCL and UPL) estimate 

and represent different parameters. It is hard to justify comparing a UCL of one population with a UPL of 

the other population. Conclusions (e.g., site dirty or site clean) derived by comparing UCLs and UPLs, or 

UCLs and upper percentiles as suggested in Wyoming DEQ, Fact Sheet #24 (2005), cannot be considered 

fair and reliable. Specifically, the decision error rates associated with such comparisons can be 

significantly different from the specified (e.g., Type I error = 0.1, Type II error = 0.1) decision errors. 

1.5 Point-by-Point Comparison of Site Observations with BTVs, Compliance 

Limits, and Other Threshold Values 

Point-by-point observation comparison method is used when a small number (e.g., 4 to 6 locations) of 

detected site observations are compared with pre-established or estimated BTVs, screening levels, or 

preliminary remediation goals (PRGs). In this case, individual point-by-point site observations (preferably 

based upon composite samples from various site locations) are compared with estimated or preestablished background (e.g., USGS values) values, PRGs, or some other not-to-exceed value. Typically, 

a single exceedance of the BTV, PRG, or a not-to-exceed value by a site (or from a monitoring well) 

observation may be considered as an indication of contamination at the site area under investigation. The 

conclusion of an exceedance by a site value is Sometimes confirmed by re-sampling (taking a few more 

collocated samples) that site location (or a monitoring well) exhibiting contaminant concentration in 

excess of the BTV or PRG. If all collocated (or collected during the same time period) sample 

observations collected from the same site location (or well) exceed the PRG (or MCL) or a not-to-exceed 

value, then it may be concluded that the location (well) requires further investigation (e.g., continuing 

treatment and monitoring) and cleanup. 

19 
When BTV contaminant concentrations are not known or pre-established, one has to collect, obtain, or 

extract a data set of an appropriate size that can be considered as representative of the site related 

background. Statistical upper limits are computed using the data set thus obtained, which are used as 

estimates of BTVs and not-to-exceed values. It should be noted that in order to compute reasonably 

reliable and accurate estimates of BTVs and not-to-exceed values based upon a background (or reference) 

data set, enough background observations (minimum of 8 to 10) should be collected, perhaps using an 

appropriate DQO process as described in EPA (2006). Typically, background samples are collected from 

a comparable general reference area or site-specific areas that are known to be free of contamination due 

to any of the site related activities. Several statistical limits can be used to estimate the BTVs based upon 

a defensible data set of an adequate size. A detailed description of the computation and estimation of 

BTVs is given in Chapter 5 (for uncensored data sets) and in Chapter 6 for data sets with nondetects of 

the revised Guidance for Comparing Background and Chemical Concentrations in Soil for CERCLA Sites 

(EPA, 2002b). Once again, the use of this point-by-point comparison method is recommended when not 

many (e.g., < 4 to 6) site observations are to be compared with estimated BTVs or PRGs. An exceedance 

of the estimated BTV by a site value may be considered as an indication of the existing or continuing 

contamination at the site. 

Note: When BTVs are not known, it is suggested that at least 8 to 10 (more are preferable) detected 

representative background observations be made available to compute reasonably reliable estimates of 

BTVs and other not-to-exceed values. 

The point-by-point comparison method is also useful when quick turnaround comparisons are required. 

Specifically, when the decisions have to be made in real time by a sampling or screening crew, or when 

few detected site samples are available, then individual point-by-point site concentrations are compared 

either with pre-established PRGs, cleanup goals and standards, or with estimated BTVs and not-to-exceed 

values. The crew can use these comparisons to make the following informative decisions: 

1. screen and identify the COPCs, 

2. identify the polluted site AOCs, 

3. continue or stop remediation or excavation at a site AOC or a RU, or 

4. move the cleanup apparatus and crew to the next AOC or RU. 

During the screening phase, an exceedance of a compliance limit, action level, a BTV, or a PRG by site 

values for a contaminant may declare that contaminant as a COPC. Those COPCs are then included in 

future site remediation and risk management studies. During the remediation phase, an exceedance of the 

threshold value such as a compliance limit (CL) or a BTV by sample values collected from a site area (or 

a monitoring well (MW)) may declare that site area as a polluted AOC, or a hot spot requiring further 

sampling and cleanup. This comparison method can also be used to verify if the site concentrations (e.g., 

from the base or side walls of an excavated site area) are approaching or meeting PRG, BTV, or a cleanup 

standard after some excavation has been conducted at that site area. 

If a larger number of detected samples (e.g., greater than 8 to10) are available from the site locations 

representing the site area under investigation (e.g., RU, AOC, EA), then the use of hypotheses testing 

approaches (both single sample and a two-sample) is preferred. The use of a hypothesis testing approach 

will control the error rates more tightly and efficiently than the individual point-by-point site observations 

20 
versus BTV comparisons, especially when many site observations are compared with a BTV or a not-toexceed value. 

Note: In background versus site comparison evaluations, scientists usually prefer the use of hypotheses 

testing approaches over point-by-point site observation comparisons with BTVs or not-to-exceed values. 

Hypotheses testing approaches require the availability of larger data sets from the populations under 

investigation. Both single sample (used when BTVs, not-to-exceed values, compliance limits, or cleanup 

standards are known and pre-established) and two-sample (used when BTVs and compliance limits are 

unknown) hypotheses testing approaches are available in ProUCL 4.0. 

1.6 Hypothesis Testing Approaches and Their Use 

Both single sample and two-sample hypotheses testing approaches are used to make cleanup decisions at 

polluted sites, and also to compare contaminant concentrations of two (e.g., site versus background) or 

more (several monitoring wells (MWs)) populations. The uses of hypotheses testing approaches in those 

environmental applications are described as follows. 

1.6.1 Single Sample Hypotheses – BTVs and Not-to-Exceed Values are Known (Preestablished) 

When pre-established BTVs and not-to-exceed values are used, such as the USGS background values 

(Shacklette and Boerngen (1984)), thresholds obtained from similar sites, pre-established threshold and 

not-to-exceed values, PRGs, or RBCs, there is no need to extract, establish, or collect a background or 

reference data set. When the BTVs and cleanup standards are known, one-sample hypotheses are used to 

compare site data (provided enough site data are available) with known and pre-established threshold 

values. It is suggested that the project team determine (e.g., using DQO) or decide (depending upon 

resources) about the number of site observations that should be collected and compared with the “preestablished” standards before coming to a conclusion about the status (clean or polluted) of the site area 

(e.g., RU, AOC) under investigation. When the number of available detected site samples is less than 4 to 

6, one might perform point-by-point site observation comparisons with a BTV; and when enough detected 

site observations (> 8 to 10, more are preferable) are available, it is desirable to use single sample 

hypothesis testing approaches. 

Depending upon the parameter (e.g., the average value, μ0, or a not-to-exceed value, A0), represented by 

the known threshold value, one can use single sample hypothesis tests for population mean (t-test, sign 

test) or single sample tests for proportions and percentiles. The details of the single sample hypotheses 

testing approaches can be found in EPA (2006) and the Technical Guide for ProUCL 4.0. Several single 

sample tests listed as follows are available in ProUCL 4.0. 

One-Sample t-Test: This test is used to compare the site mean, μ, with some specified cleanup standard, 

Cs, where the cleanup standard, Cs, represents an average threshold value, μ0. The Student’s t-test (or a 

UCL of mean) is often used (assuming normality of site data or when site sample size is large such as 

larger than 30, 50) to determine the attainment of cleanup levels at a polluted site after some remediation 

activities. 

One-Sample Sign Test or Wilcoxon Signed Rank (WSR) Test: These tests are nonparametric tests and can 

also handle nondetect observations provided all nondetects (e.g., associated detection limits) fall below 

the specified threshold value, Cs. These tests are used to compare the site location (e.g., median, mean) 

with some specified cleanup standard, Cs, representing a similar location measure. 

21 
One-Sample Proportion Test or Percentile Test: When a specified cleanup standard, A0, such as a 

PRG or a BTV represents an upper threshold value of a contaminant concentration distribution 

(e.g., not-to-exceed value, compliance limit) rather than the mean threshold value, μ0, of the contaminant 

concentration distribution, then a test for proportion or a test for percentile (or equivalently a UTL 95%­

95%, UTL 95%-90%) may be used to compare site proportion or site percentile with the specified 

threshold or action level, A0. This test can also handle ND observations provided all NDs are below the 

compliance limit. 

In order to obtain reasonably reliable estimates and test statistics, an adequate amount of representative 

site data (8 to 10 detected observations) is needed to perform the hypotheses tests. As mentioned before, 

in case only a few (e.g., < 4 to 6) detected site observations are available, then point-by-point site 

concentrations may be compared with the specified action level, A0. 

1.6.2 Two-Sample Hypotheses – When BTVs and Not-to-Exceed Values are Unknown 

When BTVs, not-to-exceed values, and other cleanup standards are not available, then site data are 

compared directly with the background data. In such cases, a two-sample hypothesis testing approach can 

be used to perform site versus background comparisons. Note that this approach can be used to compare 

concentrations of any two populations including two different site areas or two different monitoring wells 

(MWs). In order to use and perform a two-sample hypothesis testing approach, enough data should be 

available (collected) from each of the two populations under investigation. Site and background data 

requirements (e.g., based upon DQOs) to perform two-sample hypothesis test approaches are described in 

EPA (1989b, 2006), Breckenridge and Crockett (1995), and the VSP (2005) software package. While 

collecting site and background data, for better representation of populations under investigation, one may 

also want to account for the size of the background area (and site area for site samples) into sample size 

determination. That is, a larger number (>10 to 15) of representative background (or site) samples should 

be collected from larger background (or site) areas. As mentioned before, every effort should be made to 

collect as many samples as determined using DQO processes as described in EPA documents (2006). 

The two-sample (or more) hypotheses approaches are used when the site parameters (e.g., mean, shape, 

distribution) are being compared with the background parameters (e.g., mean, shape, distribution). The 

two-sample hypotheses testing approach is also used when the cleanup standards or screening levels are 

not known a priori, and they need to be estimated based upon a data set from a background or reference 

population. Specifically, two-sample hypotheses testing approaches are used to compare: 1) the average 

contaminant concentrations of two or more populations such as the background population and the 

potentially contaminated site areas, or 2) the proportions of site and background observations exceeding a 

pre-established compliance limit, A0. In order to derive reliable conclusions with higher statistical power 

based upon hypothesis testing approaches, enough data (e.g., minimum of 8 to 10 samples) should be 

available from all of the populations under investigation. It is also desirable to supplement statistical 

methods with graphical displays, such as the double Q-Q plots, or side-by-side multiple box plots, as 

available in ProUCL 4.0. Two-sample hypotheses testing approaches as incorporated in ProUCL 4.0 are 

listed as follows: 

1. Student t-test - with equal dispersions and unequal variances – Parametric test 

2. Wilcoxon-Mann-Whitney (WMW) test – Nonparametric test 

3. Gehan test – Nonparametric test 

22 
Some details of these approaches are described later in this Technical Guide. It should be noted that the 

WMW, Gehan, and quantile tests are also available for data sets with NDs. Gehan’s test is specifically 

meant to be used on data sets with multiple detection limits. It is also suggested that for best and reliable 

conclusions, both the WMW and quantile tests should be used on the same data set. The details of these 

two tests with examples are given in EPA (1994, 2006). 

The samples collected from the two (or more) populations should all be of the same type obtained using 

similar analytical methods and apparatus. In other words, the collected site and background samples 

should be all discrete or all composite (obtained using the same design and pattern), and be collected from 

the same medium (soil) at similar depths (e.g., all surface samples or all subsurface samples) and time 

(e.g., during the same quarter in groundwater applications) using comparable (preferably same) analytical 

methods. Good sample collection methods and sampling strategies are given in EPA (1996, 2003) 

guidance documents. 

1.7 Minimum Sample Size Requirements 

Due to resource limitations, it may not be possible (nor needed) to sample the entire population (e.g., 

background area, site area, areas of concern, exposure areas) under study. Statistics is used to draw 

inference(s) about the populations (clean, dirty) and their known or unknown parameters (e.g., 

comparability of population means, not-to-exceed values, upper percentiles, and spreads) based upon 

much smaller data sets (samples) collected from those populations under study. In order to determine and 

establish BTVs, not-to-exceed values, or site-specific screening levels, defensible data set(s) of 

appropriate size(s) needs to be collected from background areas (e.g., site-specific, general reference or 

pristine area, or historical data). The project team and site experts should decide what represents a site 

population and what represents a background population. The project team should determine the 

population size and boundaries based upon all current and future objectives for the data collection. The 

size and area of the population (e.g., a remediation unit, area of concern, or an exposure unit) may be 

determined based upon the potential land use, and other exposure and risk management objectives and 

decisions. Moreover, appropriate effort should be made to properly collect soil samples (e.g., methods 

based upon Gy sampling theory), as described in Gerlach and Nocerino (2003). 

Using the collected site and background data sets, statistical methods supplemented with graphical 

displays are used to perform site versus background comparisons. The test results and statistics obtained 

by performing such site versus background comparisons are used to determine if the site and background 

level contaminant concentration are comparable; or if the site concentrations exceed the background 

threshold concentration level; or if an adequate amount of cleanup and remediation approaching the BTV 

or some cleanup level have been performed at polluted areas (e.g., AOC, RU) of the site under study. 

In order to perform statistical inference (estimation and hypothesis testing), one needs to determine the 

sample sizes that need to be collected from the populations (e.g., site and background) under investigation 

using appropriate DQO processes (EPA 2006). However, in some cases, it may not be possible to collect 

the same number of samples as determined by using a DQO process. For example, the data might have 

already been collected (often is the case in practice) without using a DQO process, or due to resource 

constraints, it may not be possible to collect as many samples as determined by using a DQO based 

sample size formula. It is observed that, in practice, the project team and the decision makers may not 

collect enough background samples, perhaps due to various resource constraints. However, every effort 

should be made to collect at least 8 to 10 (more are desirable) background observations before using 

methods as incorporated in ProUCL 4.0. The minimum sample size recommendations as described here 

are useful when resources are limited (as often is the case), and it may not be possible to collect as many 

23 
background and site (e.g., AOC, EU) samples as computed using DQOs and the sample size 

determination formulae given in the EPA (2006). Some minimum sample size requirements are also given 

in Fact Sheet #24, prepared by Wyoming Department of Environmental Quality (June 2005). 

As mentioned before, the topics of DQO processes and the sample size determination are described in 

detail in the EPA (2006) guidance document. Therefore, the sample size determination formulae based 

upon DQO processes are not included in ProUCL 4.0 and its Technical Guide. However, some guidance 

and suggestions on the minimum number of background and site samples needed to be able to use 

statistical methods for the computation of upper limits, and to perform single sample tests, two-sample 

tests such as t-test and Wilcoxon- Mann-Whitney (WMW) test, and various other tests are provided here. 

The minimum sample size recommendations (requirements) as described here are made so that 

reasonably reliable estimates of EPC terms and BTVs, and defensible values of test statistics for single or 

two-sample hypotheses tests (e.g., t-test, WMW test), can be computed. 

1.7.1 Minimum Sample Size for Estimation and Point-by-Point Site Observation Comparisons 

• Point-by-point observation comparison method is used when a small number (e.g., 4 to 6 

locations) of detected site observations are compared with pre-established or estimated 

BTVs, screening levels, or PRGs. In this case, individual point-by-point site observations 

(preferably based upon composite samples from various site locations) are compared with 

estimated or pre-established background (e.g., USGS values) values, PRGs, or some 

other not-to-exceed value. 

• When BTV contaminant concentrations are not known or pre-established, one has to 

collect, obtain, or extract a data set of an appropriate size that can be considered as 

representative of the site related background. Statistical upper limits are computed using 

the data set thus obtained; which are used as estimates of BTVs and not-to-exceed values. 

It should be noted that in order to compute reasonably reliable and accurate estimates of 

BTVs and not-to-exceed values based upon a background (or reference) data set, enough 

background observations (minimum of 8 to 10) should be collected perhaps using an 

appropriate DQO process as described in EPA (2006). Typically, background samples are 

collected from a comparable general reference area or a site-specific area. 

• When enough (e.g., > 8 to 10) detected site observations are available, it is preferred to 

use hypotheses testing approaches. Specifically, single sample hypotheses testing 

(comparing site to a specified threshold) approaches should be used to perform site 

versus a known threshold comparison and two-sample hypotheses testing (provided 

enough background data are also available) approaches should be used to perform site 

versus background comparison. 

1.7.2 Minimum Sample Sizes for Hypothesis Testing 

Statistical methods (as in ProUCL 4.0) used to estimate EPC terms, BTVs, PRGs, or to compare the site 

contaminant concentration data distribution with the background data distribution can be computed based 

upon small site and background data sets (e.g., of sizes 3, 4, 5, or 6). However, those statistics cannot be 

considered representative and reliable enough to make important cleanup and remediation decisions. It is 

recommended not to use those statistics to draw cleanup and remediation decisions potentially impacting 

the human health and the environment. It is suggested that the estimation and hypothesis testing methods 

as incorporated in ProUCL 4.0 may not be used on background data sets with fewer than 8 to 10 detected 

24 
observations. Also, when using hypotheses testing approaches, it is suggested that the site and 

background data be obtained using an appropriate DQO process as described in EPA (2006). In case that 

is not possible, it is suggested that the project team at least collect 8 to 10 observations from each of the 

populations (e.g., site area, MWs, background area) under investigation. 

Site versus background comparisons and computation of the BTVs depend upon many factors, some of 

which cannot be controlled. These factors include the site conditions, lack of historical information, site 

medium, lack of adequate resources, measurement and analytical errors, and accessibility of the site areas. 

Therefore, whenever possible, it is desirable to use more than one statistical method to perform site versus 

background comparison. The use of statistical methods should always be supplemented with appropriate 

graphical displays. 

1.7.3 Sample Sizes for Bootstrap Methods 

Several parametric and nonparametric (including bootstrap methods) UCL, UPL, and other limits 

computation methods for both full-uncensored data sets (without nondetects) and left-censored data sets 

with nondetects are available in ProUCL 4.0. It should be noted that bootstrap resampling methods are 

useful when not too few (e.g., < 10-15) and not too many (e.g., > 500-1000) detected observations are 

available. For bootstrap methods (e.g., percentile method, BCA bootstrap method, bootstrap t method), a 

large number (e.g., 1000, 2000) of bootstrap resamples (with replacement) are drawn from the same data 

set. Therefore, in order to obtain bootstrap resamples with at least some distinct values (so that statistics 

can be computed from each resample), it is suggested that a bootstrap method should not be used when 

dealing with small data sets of sizes less than 10-15. Also, it is not required to bootstrap a large data set of 

size greater than 500 or 1000; that is when a data set of a large size (e.g., > 1000) is available, there is no 

need to obtain bootstrap resamples to compute statistics of interest (e.g., UCLs). One can simply use a 

statistical method on the original large data set. Moreover, bootstrapping a large data set of size greater 

than 500 or 1000 will be time consuming. 

1.8 Statistical Analyses by a Group ID 

The analyses of data categorized by a group ID variable such as: 1) Surface vs. Subsurface; 

2) AOC1 vs. AOC2; 3) Site vs. Background; and 4) Upgradient vs. Downgradient monitoring wells are 

quite common in many environmental applications. ProUCL 4.0 offers this option for data sets with and 

without nondetects. The Group Option provides a powerful tool to perform various statistical tests and 

methods (including graphical displays) separately for each of the group (samples from different 

populations) that may be present in a data set. For an example, the same data set may consist of samples 

from the various groups or populations representing site, background, two or more AOCs, surface, 

subsurface, monitoring wells. The graphical displays (e.g., box plots, Q-Q plots) and statistics 

(background statistics, UCLs, hypotheses testing approaches) of interest can be computed separately for 

each group by using this option. 

It should be pointed out that it is the users’ responsibility to provide adequate amount of detected data to 

perform the group operations. For an example, if the user desires to produce a graphical Q-Q plot (using 

only detected data) with regression lines displayed, then there should be at least two detected points (to 

compute slope, intercept, sd) in the data set. Similarly if the graphs are desired for each of the group 

specified by the group ID variable, there should be at least two detected observations in each group 

specified by the group variable. ProUCL 4.0 generates a warning message (in orange color) in the lower 

panel of the ProUCL 4.0 screen. Specifically, the user should make sure that a variable with nondetects 

25 
and categorized by a group variable should have enough detected data in each group to perform the 

various methods (e.g., GOF tests, Q-Q plots with regression lines) as incorporated in ProUCL 4.0. 

1.9 Use of Maximum Detected Value as Estimates of Upper Limits 

Some practitioners tend to use the maximum detected value as an estimate of the EPC term. This is 

especially true when the sample size is small such as ≤ 5, or when a UCL95 exceeds the maximum 

detected values (EPA, 1992b). Also, many times in practice, the BTVs and not-to-exceed values are 

estimated by the maximum detected value. This section discusses the appropriateness of using the 

maximum detected value as estimates of the EPC term, BTVs, or other nor-to-exceed values. 

1.9.1 Use of Maximum Detected Value to Estimate BTVs and Not-to-Exceed Values 

It is noted that BTVs and not-to-exceed values represent upper threshold values in the upper tail of a data 

distribution; therefore, depending upon the data distribution and sample size, the BTVs and other not-toexceed values may be estimated by the maximum detected value. As described earlier, upper limits, such 

as UPLs, UTLs, and upper percentiles, are used to estimate the BTVs and not-to-exceed values. It is noted 

that a nonparametric UPL or UTL is often estimated by higher order statistics such as the maximum value 

or the second largest value (EPA 1992a, RCRA Guidance Addendum). The use of higher order statistics 

to estimate the UTLs depends upon the sample size. For an example: 1) 59 to 92 samples, a 

nonparametric UTL95%-95 is given by the maximum detected value; 2) 93 to 123 samples, a 

nonparametric UTL95%-95 is given by the second largest maximum detected value; and 3) 124 to 152 

samples, a UTL95%-95 is given by the third largest detected value in the sample. 

Note: Therefore, when a data set does not follow a discernable distribution, the maximum observed value 

(or other high order statistics such as the second largest, third largest) may be used as an estimate of 

BTV or a not-to-exceed value, provided the maximum value does not represent an outlier or a 

contaminating observation perhaps representing a hot location. The selection of a higher order statistic 

(e.g., largest, second largest, third largest) to estimate BTV depends upon the sample size and confidence 

coefficient. 

1.9.2 Use of Maximum Detected Value to Estimate EPC Terms 

This issue was also discussed in the ProUCL 3.0 Technical Guide (EPA, 2004). Some practitioners tend 

to use the maximum detected value as an estimate of the EPC term. This is especially true when the 

sample size is small such as ≤ 5, or when a UCL95 exceeds the maximum detected values (EPA, 1992b). 

Specifically, a RAGS document (EPA, 1992) suggests the use of the maximum detected value as a default 

value to estimate the EPC term when a 95% UCL (e.g., the H-UCL) exceeded the maximum value. 

ProUCL 3.0 and ProUCL 4.0 can compute a 95% UCL of mean using several methods based upon 

normal, Gamma, lognormal, and non-discernable distributions. In past (e.g., EPA, 1992b), only two 

methods were used to estimate the EPC term based upon: 1) Student’s t-statistic and a normal 

distribution, and 2) Land’s H-statistic (1975) and a lognormal model. The use of H-statistic often yields 

unstable and impractically large UCL95 of the mean (Singh, Singh, and Iaci, 2002). For skewed data sets 

of smaller sizes (e.g., < 30, < 50), H-UCL often exceeds the maximum detected value. This is especially 

true when some extreme high outliers may be present in the data set. Since the use of a lognormal 

distribution has been quite common (e.g., suggested as a default model in a RAGS document (EPA, 

1992)), the exceedance of the maximum detected value by H-UCL95 is frequent for many skewed data 

sets of smaller sizes (e.g., < 30, < 50). It is also be noted that for highly skewed data sets, the sample 

26 
mean indeed can even exceed the upper 90%, 95%, etc., percentiles, and consequently, a 95% UCL of 

mean can exceed the maximum observed value of a data set. 

All of these occurrences result in the possibility of using the maximum detected value as an estimate of 

the EPC term. It should be pointed out that in some cases, the maximum observed value actually might 

represent a highly polluted outlying observation. Obviously, it is not desirable to use a highly polluted 

value as an estimate of average exposure (EPC term) for an exposure area. This is especially true when 

one is dealing with lognormally distributed data sets of small sizes. As mentioned before, for such highly 

skewed data sets that cannot be modeled by a gamma distribution, a 95% UCL of the mean should be 

computed using an appropriate distribution-free nonparametric method. 

It should be pointed out that the EPC term represents the average exposure contracted by an individual 

over an exposure area (EA) during a long period of time; therefore, the EPC term should be estimated by 

using an average value (such as an appropriate 95% UCL of the mean) and not by the maximum observed 

concentration. One needs to compute an average exposure and not the maximum exposure. It is unlikely 

that an individual will visit the location (e.g., in an EA) of the maximum detected value all of the time. 

One can argue that the use of this practice results in a conservative (higher) estimate of the EPC term. The 

objective is to compute an accurate estimate of the EPC term. Today, several other methods (instead of HUCL) as described in EPA (2002), and included in ProUCL 3.0 (EPA 2004) and ProUCL 4.0 (EPA 

2007), are available which can be used to estimate the EPC terms. It is unlikely (but possible with 

outliers) that the UCLs based upon those methods will exceed the maximum detected value, unless some 

outliers are present in the data set. ProUCL 4.0 displays a warning message when the recommended 95% 

UCL (e.g., Hall’s or bootstrap t UCL with outliers) of the mean exceeds the observed maximum 

concentration. When a 95% UCL does exceed the maximum observed value, ProUCL4.0 recommends the 

use of an alternative UCL computation method based upon the Chebyshev inequality. One may use a 

97.5% or 99% Chebyshev UCL to estimate the mean of a highly skewed population. It should be noted 

that typically, a Chebyshev UCL yield conservative (but stable) and higher values of the UCLs than other 

methods available in ProUCL 4.0. This is especially true when data are moderately skewed and sample 

size is large. In such cases, when the sample size is large (and other UCL methods such as bootstrap t 

method yield unrealistically high values), one may want to use a 95% Chebyshev UCL or a Chebyshev 

UCL with lower confidence coefficient such as 92.5% or 90% as estimate of the population mean, 

especially when the sample size is large (e.g., >100, 150). The detailed recommendations (as functions of 

sample size and skewness) for the use of those UCLs are summarized in ProUCL 3.0 Technical Guide 

(EPA, 2004). 

Singh and Singh (2003) studied the performance of the max test (using the maximum observed value as 

an estimate of the EPC term) via Monte Carlo simulation experiments. They noted that for skewed data 

sets of small sizes (e.g., < 10-20), the max test does not provide the specified 95% coverage to the 

population mean, and for larger data sets, it overestimates the EPC term, which may require unnecessary 

further remediation. This can also be viewed in the graphs presented in ProUCL 3.0 Technical Guide. The 

use of the maximum value as an estimate of the EPC term also ignores most (except for maximum value) 

of the information contained in the data set. 

With the availability of so many UCL computation methods (15 of them), the developers of ProUCL 4.0 

do not feel any need to use the maximum observed value as an estimate of the EPC term representing an 

average exposure by an individual over an EA. Also, for the distributions considered, the maximum value 

is not a sufficient statistic for the unknown population mean. 

27 
Note: It is recommended that the maximum observed value NOT be used as an estimate of the EPC term 

representing average exposure contracted by an individual over an EA. For the sake of interested users, 

ProUCL displays a warning message when the recommended 95% UCL (e.g., Hall’s bootstrap UCL, etc.) 

of the mean exceeds the observed maximum concentration. For such scenarios (when a 95% UCL does 

exceed the maximum observed value), an alternative 95% UCL computation method is recommended by 

ProUCL 4.0. 

1.10 Samples with Nondetect Observations 

Nondetect observations (or less than obvious values) are inevitable in most environmental data sets. 

Singh, Maichle, and Lee (EPA, 2006) studied the performances (in terms of coverages) of the various 

UCL95 computation methods including the simple substitution methods (such as the DL/2 and DL 

methods) for data sets with nondetect observations. They concluded that the UCLs obtained using the 

substitution methods, including the replacement of nondetects by respective DL/2, do not perform well 

even when the percentage of nondetect observations is low, such as 5%-10%. They recommended 

avoiding the use of substitution methods to compute UCL95 based upon data sets with nondetect 

observations. 

1.10.1 Avoid the Use of DL/2 Method to Compute UCL95 

Based upon the results of the report by Singh, Maichle, and Lee (EPA, 2006), it is strongly recommended 

to avoid the use of the DL/2 method to perform GOF test, and to compute the summary statistics and 

various other limits (e.g., UCL, UPL) often used to estimate the EPC terms and BTVs. Until recently, the 

DL/2 method has been the most commonly used method to compute the various statistics of interest for 

data sets with BDL observations. The main reason of its common use has been the lack of the availability 

of other defensible methods and associated programs that can be used to estimate the various 

environmental parameters of interest. Today, several other methods (e.g., KM method, bootstrap 

methods) with better performances are available that can be used to compute the various upper limits of 

interest. Some of those parametric and nonparametric methods are now available in ProUCL 4.0. Even 

though the DL/2 method (to compute UCLs, UPLs, and for goodness-of-fit test) has also been 

incorporated in ProUCL 4.0, its use is not recommended due to its poor performance. The DL/2 method 

is included in ProUCL 4.0 only for historical reasons as it had been the most commonly used and 

recommended method until recently (EPA, 2006). Some of the reviewers of ProUCL 4.0 suggested and 

requested the inclusion of DL/2 method in ProUCL for comparison purposes. 

Note: The DL/2 method has been incorporated in ProUCL 4.0 for historical reasons only. NERL-EPA, 

Las Vegas strongly recommends avoiding the use of DL/2 method even when the percentage (%) of NDs 

is as low as 5%-10%. There are other methods available in ProUCL 4.0 that should be used to compute 

the various summary statistics and upper limits based upon data sets with single and multiple detection 

limits. 

1.11 Samples with Low Frequency of Detection 

When all of the sampled data values are reported as nondetects, the EPC term should also be reported as a 

nondetect value, perhaps by the maximum reporting limit (RL) or maximum RL/2. Statistics (e.g., 

UCL95) computed based upon only a few detected values (e.g., < 4 to 6) cannot be considered reliable 

enough to estimate the EPC terms having potential impact on the human heath and the environment. 

When the number of detected data is small, it is preferable to use simple ad hoc methods rather than using 

statistical methods to compute the EPC terms and other upper limits. Specifically, it is suggested that in 

28 
cases when the detection frequency is low (e.g., < 4%-5%) and the number of detected observations is 

low, the project team and the decision makers together should make a decision on site-specific basis on 

how to estimate the average exposure (EPC term) for the contaminant and area under consideration. For 

such data sets with low detection frequencies, other measures such as the median or mode represent better 

estimates (with lesser uncertainty) of the population measure of central tendency. 

Additionally, it is also suggested that when most (e.g., > %95) of the observations for a contaminant lie 

below the detection limit(s) or reporting limits (RLs), the sample median or the sample mode (rather than 

the sample average which cannot be computed accurately) may be used as an estimate the EPC term. Note 

that when the majority of the data are nondetects, the median and the mode will also be a nondetect. The 

uncertainty associated with such estimates will be high. It is noted that the statistical properties, such as 

the bias, accuracy, and precision of such estimates, would remain unknown. In order to be able to 

compute defensible estimates, it is always desirable to collect more samples. 

Note: In case the number of available detected samples is small (< 5), it is suggested that the project 

team decide about the estimation of the EPC term on site-specific basis. For such small data sets with 

very few detected values (< 5), the final decision (“policy decision”) on how to estimate the EPC term 

should be determined by the project team and decision makers. 

1.12 Some Other Applications of Methods in ProUCL 4.0 

In addition to performing background versus site comparisons for CERCLA and RCRA sites, and 

estimating the EPC terms in exposure and risk evaluation studies, the statistical methods as incorporated 

in ProUCL 4.0 can be used to address other issues dealing with environmental investigations that are 

conducted at Superfund or RCRA sites. 

1.12.1 Identification of COPCs 

Risk assessors and RPMs often use screening levels or BTVs to identify the COPCs during the screening 

phase of a cleanup project to be conducted at a contaminated site. The screening for the COPCs is 

performed prior to any characterization and remediation activities that may have to be conducted at the 

site under investigation. This comparison is performed to screen out those contaminants that may be 

present in the site medium of interest at low levels (e.g., at or below the background levels or some preestablished screening levels) and may not pose any threat and concern to human health and the 

environment. Those contaminants may be eliminated from all future site investigations, and risk 

assessment and risk management studies. 

In order to identify the COPCs, point-by-point site observations (preferably composite samples) are 

compared with some pre-established screening levels, SSL, or estimated BTVs. This is especially true 

when the comparisons of site concentrations with screening levels or BTVs are conducted in real time by 

the sampling or cleanup crew right there in the site field. The project team should decide about the type of 

site samples (discrete or composite) and the number of detected site observations (not more than 4 to 6) 

that should be collected and compared with the screening levels or the BTVs. In case BTVs, screening 

levels, or not-to-exceed values are not known, the availability of a defensible background or reference 

data set of reasonable size (e.g., > 8 to 10, more are preferable) is required to obtain reliable estimates of 

BTVs and screening levels. When a reasonable number of detected site observations are available, it is 

preferable to use hypotheses testing approaches. The contaminants with concentrations exceeding the 

respective screening values or BTVs may be considered as COPCs, whereas contaminants with 

29 
concentrations (in all collected samples) lower than the screening value, PRG, or an estimated BTV may 

be omitted from all future evaluations including the risk assessment and risk management investigations. 

1.12.2 Identification of Non-Compliance Monitoring Wells 

In monitoring well (MW) compliance assessment applications, individual (often discrete) contaminant 

concentrations from a MW are compared with some pre-established ACL, MCL, or an estimated 

compliance limit (CL) based upon a group of upgradient wells representing the background population. 

An exceedance of the MCL or the BTV by a MW concentration may be considered as an indication of 

contamination in that MW. In such individual concentration comparisons, the presence of contamination 

(determined by an exceedance) may have to be confirmed by re-sampling from that MW. If 

concentrations of contaminants in both the original sample and the re-sample(s) exceed the MCL or BTV, 

then that MW may require closer scrutiny, perhaps triggering the remediation remedies as determined by 

the project team. If the concentration data from a MW for about 4 to 5 continuous quarters (or some other 

designated time period determined by the project team) are below the MCL or BTV level, then that MW 

may be considered as complying with (achieving) the pre-established or estimated standards. Statistical 

methods as described in Chapters 5 and 6 of the revised Guidance for Comparing Background and 

Chemical Concentrations in Soil for CERCLA Sites (EPA, 2002b) can be used to estimate the not-toexceed values or BTVs based upon background or upgradient wells in case the ACLs or MCLs are not 

pre-determined. 

1.12.3 Verification of the Attainment of Cleanup Standards, Cs 

Hypothesis testing approaches may be used to verify the attainment of the cleanup standard, Cs, at 

polluted site areas of concern after conducting remediation and cleanup at the site AOC (EPA, 2006). In 

order to properly address this scenario, a site data set of adequate size (minimum of 8 to 10 detected site 

observations) needs to be made available from the remediated or excavated areas of the site under 

investigation. The sample size should also account for the size of the remediated site area; meaning that 

larger site areas should be sampled more (with more observations) to obtain a representative sample of the 

site under investigation. 

Typically, the null hypothesis of interest is H0: Site Mean, μs >= Cs versus the alternative hypothesis, H1: 

Site Mean, μs < Cs, where the cleanup standard, Cs, is known a priori. The sample size needed to perform 

such single sample hypotheses tests can be obtained using the DQO process-based sample size formula as 

given in the EPA (2006) documents. In any case, in order to use this test, a minimum of 8 to 10 detected 

site samples should be collected. The details of the statistical methods used to perform single sample 

hypothesis as described above can be found in EPA (2006). 

1.12.4 Using BTVs (Upper Limits) to Identify Hot Spots 

The use of upper limits (e.g., UTLs) to identify hot spot(s) has also been mentioned in the Guidance for 

Comparing Background and Chemical Concentrations in Soil for CERCLA Sites (EPA, 2002b). Point-bypoint site observations (preferably using composite samples representing a site location) are compared 

with a pre-established or estimated BTV. Exceedances of the BTV by site observations may be 

considered as representing locations with elevated concentrations (hot spots). Chapters 5 and 6 of the 

revised Guidance for Comparing Background and Chemical Concentrations in Soil for CERCLA Sites 

(EPA, 2002b) describe several methods to estimate the BTVs based upon data sets without nondetects 

(NDs) and left-censored data sets with NDs. 

30 
The rest of the chapters of this Technical Guide briefly describe the various statistical methods as 

incorporated in ProUCL 4.0. Those methods are useful to analyze environmental data sets with and 

without the nondetect observations. It should be noted that ProUCL 4.0 is the first software package 

equipped with single sample and two-sample hypotheses testing approaches that can be used on data sets 

with nondetect observations. 

Note: It should be pointed out that while developing ProUCL 4.0, emphasis is given to the practical 

applicability of the estimation and hypotheses testing methods as incorporated in ProUCL 4.0. Also, it 

should be noted that ProUCL 4.0 does provide many graphical and statistical methods often used in the 

various statistical applications. ProUCL 4.0 does not provide statistical methods that may be used to 

compute sample sizes based upon DQO processes (EPA, 2006). Those sample size determination methods 

are available in other freeware packages such as VSP (2005) and DataQUEST (EPA, 1997). However, as 

mentioned before, some practical guidance on the minimum sample size requirements to be able to use 

methods as available in ProUCL 4.0 has been provided in Chapter 1. Similar statements and suggestions 

have been made throughout this Technical Guide. 

31 
32 
Chapter 2 

Methods for Computing (1 – α)100% UCL of Mean for Data Sets 

without Nondetect Observations as Incorporated in ProUCL 4.0 

Software 

2.1 Introduction 

Exposure assessment and cleanup decisions in support of U.S. EPA projects are often made based upon 

the mean concentrations of the contaminants of potential concern. A 95% upper confidence limit (UCL) 

of the unknown population arithmetic mean (AM), μ1, is often used to: estimate the exposure point 

concentration (EPC) term (EPA, 1992; EPA, 2002), determine the attainment of cleanup standards (EPA, 

1989, and EPA, 1991), estimate background level contaminant concentrations, or compare the soil 

concentrations with site-specific soil screening levels (EPA, 1996). It is, therefore, important to compute a 

reliable, conservative, and stable 95% UCL of the population mean using the available data. The 95% 

UCL should approximately provide the 95% coverage for the unknown population mean, μ1. EPA 

(2002a) has developed a guidance document for calculating upper confidence limits based upon full data 

sets without nondetect observations. Most of those UCL computation methods as described in the EPA 

(2002a) guidance document are available in ProUCL 3.0. ProUCL 3.0 can also compute 95% UCLs of the 

mean based upon the gamma distribution which is better suited to model positively skewed environmental 

data sets. ProUCL 4.0 represents an upgrade of ProUCL 3.0. Specifically, ProUCL 4.0 provides several 

parametric and nonparametric UCL computation methods for data sets with nondetect (ND) observations. 

Therefore, this Technical Guide is an upgrade of the technical guide associated with ProUCL 3.0. The 

capabilities and methods as incorporated in ProUCL 3.0 are also available in ProUCL 4.0. Parametric and 

nonparametric UCL computation methods as incorporated in ProUCL 4.0 for data sets with nondetect 

observations are described in Chapter 4 of this Technical Guide. The details of those UCL computation 

methods can be found in Singh, Maichle, and Lee (EPA, 2006). 

Chapter 2 describes the UCL methods for full data sets without ND observations as incorporated in 

ProUCL 3.0 Technical Guide. Computation of a (1 – α)100% UCL of the population mean depends upon 

the data distribution. Typically, environmental data are positively skewed, and a default lognormal 

distribution (EPA, 1992) is often used to model such data distributions. The H-statistic-based Land’s 

(Land, 1971, 1975) H-UCL of the mean is used in these applications. Hardin and Gilbert (1993), Singh, 

Singh, and Engelhardt (1997, 1999), Schultz and Griffin (1999), and Singh, Singh, and Iaci (2002b) 

pointed out several problems associated with the use of the lognormal distribution and the H-UCL of the 

population AM. In practice, for lognormal data sets with high standard deviation (Sd), σ, of the natural 

log-transformed data (e.g., σ exceeding 2.0), the H-UCL can become unacceptably large, exceeding the 

95% and 99% data quantiles, and even the maximum observed concentration, by orders of magnitude 

(Singh, Singh, and Engelhardt, 1997). This is especially true for skewed data sets of sizes smaller than n < 

50-70. 

The H-UCL is also very sensitive to a few low or high values. For example, the addition of a sample with 

below detection limit measurement can cause the H-UCL to increase by a large amount (Singh, Singh, 

and Iaci, 2002b). Realizing that the use of H-statistic can result in unreasonably large UCL, it has been 

recommended (EPA, 1992) to use the maximum observed value as an estimate of the UCL (EPC term) in 

cases where the H-UCL exceeds the maximum observed value. The issue of the use of the maximum 

detected value as an estimate of the EPC term has been discussed earlier in Chapter 1. There are 15 UCL 

computation methods available in ProUCL; 5 are parametric and 10 are nonparametric. The 

33 
nonparametric methods do not depend upon any of the data distributions. For full data sets without any 

nondetect observations, ProUCL 4.0 (and also ProUCL 3.0) makes recommendations on how to compute 

an appropriate UCL95. Those recommendations are made based upon the findings of extensive simulation 

study conducted by Singh and Singh (2003). 

It is noted that both lognormal and gamma distributions can be used to model positively skewed data sets. 

It is also noted that it is hard to distinguish between a lognormal and a gamma distribution, especially 

when the sample size is small, such as n < 50 - 70. In practice, many skewed data sets follow a lognormal 

as well as a gamma distribution. Singh, Singh, and Iaci (2002b) observed that the UCL based upon a 

gamma distribution results in reliable and stable values of practical merit. It is, therefore, desirable to test 

if an environmental data set follows a gamma distribution. For data sets (of all sizes) which follow a 

gamma distribution, the EPC should be computed using an adjusted gamma UCL (when 0.1 ≤ k < 0.5) of 

the mean or an approximate gamma UCL (when k ≥ 0.5) of the mean, as these UCLs approximately 

provide the specified 95% coverage to the population mean, μ1 = kθ, gamma distribution. For values of k 

< 0.1, a 95% UCL may be obtained using bootstrap t-method or Hall’s bootstrap method when the sample 

size, n is less than 15, and for larger samples, a UCL of the mean may be computed using the adjusted or 

approximate gamma UCL. Here, k is the shape parameter of a gamma distribution as described in later in 

this chapter. 

It should be pointed out that both bootstrap t and Hall’s bootstrap methods sometimes result in erratic, 

inflated, and unstable UCL values, especially in the presence of outliers (Efron and Tibshirani, 1993). 

Therefore, these two methods should be used with caution. The user should examine the various UCL 

results and determine if the UCLs based upon the bootstrap t and Hall’s bootstrap methods represent 

reasonable and reliable UCL values of practical merit. If the results based upon these two methods are 

much higher than the rest of methods (except for the UCLs based upon lognormal distribution), then this 

could be an indication of erratic UCL values. ProUCL prints out a warning message whenever the use of 

these two bootstrap methods is recommended. In case these two bootstrap methods yield erratic, unstable, 

and inflated UCLs, the UCL of the mean may be computed using the adjusted or the approximate gamma 

UCL computation method, or based upon the Chebyshev inequality. 

ProUCL 4.0 has goodness-of-fit (GOF) methods to test for normality, lognormality, and a gamma 

distribution of a data set with and without nondetect observations. Depending upon the data distribution, 

ProUCL 4.0 can be used to compute a conservative and stable 95% UCL of the population mean, μ1, and 

various other upper limits (e.g., UPLs, UTLs) for data sets with and without the nondetect observations. 

The critical values of the Anderson-Darling test statistic and the Kolmogorov-Smirnov test statistic to test 

for gamma distribution were generated using Monte Carlo simulation experiments. Those critical values 

are tabulated in Appendix A for various levels of significance. Singh, Singh, and Engelhardt (1997, 

1999); Singh, Singh, and Iaci (2002b); and Singh and Singh (2003) studied several parametric and 

nonparametric UCL computation methods that have been included in ProUCL 4.0. Most of the 

mathematical algorithms and formulae used in ProUCL to compute the various statistics are summarized 

in this chapter. ProUCL computes the various summary statistics for raw, as well as log-transformed data 

sets with and without nondetect observations. In this Technical Guide and in ProUCL, log-transform (log) 

stands for the natural logarithm (ln) to the base e. ProUCL also computes the maximum likelihood 

estimates (MLEs) and the minimum variance unbiased estimates (MVUEs) of various unknown 

population parameters of normal, lognormal, and gamma distributions. For full data sets without 

nondetect observations, ProUCL 4.0 (and also ProUCL 3.0) computes the (1 – α)100% UCLs of the 

unknown population mean, μ1, using five (5) parametric and ten (10) nonparametric methods, which are 

described in section 2.4 of this chapter. 

34 
For data sets without NDs, comparisons of the performances of the UCL computation methods (in terms 

of coverage probabilities) were performed by Singh and Singh (2003) and Singh et al. (2006). It is also 

well known that the Jackknife method (with sample mean as an estimator) and Student’s t-method yield 

identical UCL values. Moreover, it is noted that the standard bootstrap method and the percentile 

bootstrap method do not perform well (do not provide adequate coverage) for skewed data sets. However, 

for the sake of completeness, all of the parametric as well as nonparametric methods have been included 

in ProUCL 4.0. Also, it was noted that the omission of a method such as the Jackknife method or the biascorrected accelerated (BCA) bootstrap method triggers the curiosity of some of the users as they may 

think that the omitted method might perform better than the various other methods already incorporated in 

ProUCL. In order to satisfy all users, ProUCL 4.0 provides most of the bootstrap UCL computation 

methods. 

2.2 Goodness-of-Fit (GOF) Test Procedures to Test for a Data Distribution 

Let x1, x2, ... , xn be a random sample (e.g., representing lead concentrations) from the underlying 

population (e.g., remediated part of a site) with unknown mean, μ1, and variance, σ1

2

. Let μ and σ

represent the population mean and the population standard deviation (Sd) of the log-transformed (natural 

log to the base e) data. Let y and sy (= σˆ ) be the sample mean and sample Sd, respectively, of the logtransformed data, yi = log (xi); i = 1, 2, ... , n. Specifically, let 

1 n 

∑

i =1 

2 

y = y (2-1) i

n 

1 n 

∑

i =1 

Similarly, let x and sx be the sample mean and Sd of the raw data, x1 , x2 , .. , xn, obtained by replacing y 

by x in equations (2-1) and (2-2), respectively. In this Technical Guide, irrespective of the underlying 

distribution, μ1, and σ1

2

 represent the mean and variance of the random variable X (in original units), 

whereas μ and σ

2

 represent the mean and variance of its logarithm, given by Y = loge(X) = natural 

logarithm. 

Three data distributions have been considered in ProUCL 4.0. These include the normal, lognormal, and 

the gamma distributions. Shapiro-Wilk (n ≤ 50) and Lilliefors (n > 50) test statistics are used to test for 

normality or lognormality of a data set. It should be noted that even though Shapiro-Wilk (S-W) test has 

been extended up to samples of size 2000 (Royston, 1982), ProUCL 4.0 provides S-W test only for 

samples of sizes up to 50. Lilliefors test (along with graphical Q-Q plot) seems to perform fairly well for 

samples of size 50 and higher. The empirical distribution function (EDF)-based methods: the 

Kolmogorov-Smirnov (K-S) test and the Anderson-Darling (A-D) test are used to test for a gamma 

distribution. Extensive critical values for these two test statistics have been obtained via Monte Carlo 

simulation experiments. For interested users, these critical values are given in the Appendix for various 

levels of significance. In addition to these formal tests, the informal histogram and quantile-quantile (QQ) plot are also available to visually test data distributions. Q-Q plots also provide useful information 

about the presence of potential outliers and multiple populations. A brief description of these GOF tests 

follows. 

2 2

σˆ = s = ( y − y) (2-2) y i

n −1

35 
2.2.1 Test Normality and Lognormality of a Data Set 

ProUCL tests for normality or lognormality of a data set using three different methods described below. 

The program tests normality or lognormality at three different levels of significance, namely, 0.01, 0.05, 

and 0.1. The details of those methods can be found in the cited references below. 

2.2.1.1 Normal Quantile-Quantile (Q-Q) Plot 

This is a simple informal graphical method to test for an approximate normality or lognormality of a data 

distribution (Hoaglin, Mosteller, and Tukey 1983; Singh 1993). A linear pattern displayed by the bulk of 

the data suggests approximate normality or lognormality (performed on log-transformed data) of the data 

distribution. For example, a high value (e.g., 0.95 or greater) of the correlation coefficient of the linear 

pattern may suggest approximate normality (or lognormality) of the data set under study. However, it 

should be noted that on this graphical display, observations well separated (sticking out) from the linear 

pattern displayed by the bulk data represent the outlying observations. Also, apparent jumps and breaks in 

the Q-Q plot suggest the presence of multiple populations. The correlation coefficient of such a Q-Q plot 

can still be high, which does not necessarily imply that the data follow a normal (or lognormal) 

distribution. Therefore, the informal graphical Q-Q plot test should always be accompanied by other more 

powerful tests, such as the Shapiro-Wilk test or the Lilliefors test. The goodness-of-fit (GOF) test of a 

data set should always be judged based upon the formal as well as informal graphical displays. The 

normal Q-Q plot may be used as an aid to identify outliers or to identify multiple populations. ProUCL 

performs the graphical Q-Q plot test on raw data as well as on standardized data. All relevant statistics, 

such as the correlation coefficient, are also displayed on the Q-Q plot. 

2.2.1.2 Shapiro-Wilk W Test 

This is a powerful test and is often used to test the normality or lognormality of the data set under study 

(Gilbert 1987). ProUCL performs this test for samples of size 50 or smaller. The Scout (EPA 1999) 

software package (currently under revision and upgrade) will extend the S-W test for samples of size up 

to 2000. Based upon the selected level of significance and the computed test statistic, ProUCL also 

informs the user if the data are normally (or lognormally) distributed. This information should be used to 

obtain an appropriate UCL of the mean. The program prints the relevant statistics (such as the S-W test 

statistic, slope, and correlation) on the Q-Q plot of the data. For convenience, normality, lognormality, or 

gamma distribution test results for 0.05 level of significance are also displayed on the Excel-type output 

summary sheets. 

2.2.1.3 Lilliefors Test 

This test is useful for data sets of larger size (Dudewicz and Misra, 1988, Conover, 1999). ProUCL 

performs this test for samples of sizes up to 1000. Based upon the selected level of significance and the 

computed test statistic, ProUCL informs the user if the data are normally (or lognormally) distributed. 

The user should use this information to obtain an appropriate UCL of the mean. The program prints the 

relevant statistics on the Q-Q plot of data. For convenience, normality, lognormality, or gamma 

distribution test results for 0.05 level of significance are also displayed on the UCL output summary 

sheets. It should be pointed out that sometimes, in practice, these two goodness-of-fit tests could lead to 

different conclusions. 

36 
2.2.2 Gamma Distribution 

Singh, Singh, and Iaci (2002b) studied gamma distributions to model positively skewed environmental 

data sets and to compute a UCL of the mean based upon a gamma distribution. They studied several UCL 

computation methods using Monte Carlo simulation experiments. A continuous random variable, X (e.g., 

concentration of a contaminant), is said to follow a gamma distribution, G (k, θ) with parameters k > 0 

(shape parameter) and θ > 0 (scale parameter), if its probability density function is given by the following 

equation: 

f (x; k,θ) = θ k Γ

1

(k) 

x

k −1

e −x θ; x > 0 

(2-3) 

= 0; otherwise 

The parameter, k, is the shape parameter, and θ is the scale parameter. Many positively skewed data sets 

follow a lognormal as well as a gamma distribution. Gamma distributions can be used to model positively 

skewed environmental data sets. It is observed that the use of a gamma distribution results in reliable and 

stable 95% UCL values. It is therefore, desirable to test if an environmental data set follows a gamma 

distribution. If a skewed data set does follow a gamma model, then a 95% UCL of the population mean 

should be computed using a gamma distribution. For details of the two gamma goodness-of-fit tests, 

maximum likelihood estimation of gamma parameters, and the computation of a 95% UCL of the mean 

based upon a gamma distribution, refer to D’Agostino and Stephens (1986), and Singh, Singh, and Iaci 

(2002). These methods are briefly described as follows. 

For data sets which follow a gamma distribution, the adjusted 95% UCL of the mean based upon a 

gamma distribution is optimal and approximately provides the specified 95% coverage to population 

mean, μ1 = kθ (Singh, Singh, and Iaci (2002)). Moreover, this adjusted gamma UCL yields reasonable 

numbers of practical merit. The two test statistics used for testing for a gamma distribution are based upon 

the empirical distribution function (EDF). The two EDF tests included in ProUCL are the KolmogorovSmirnov (K-S) test and Anderson-Darling (A-D) test, which are described in D’Agostino and Stephens 

(1986) and Stephens (1970). The graphical Q-Q plot for gamma distribution has also been included in 

ProUCL. The critical values for the two EDF tests are not easily available, especially when the shape 

parameter, k, is small (k < 1). Therefore, the associated critical values have been obtained via extensive 

Monte Carlo simulation experiments. These critical values for the two test statistics are given in Appendix 

A. The 1%, 5%, and 10% critical values of these two test statistics have been incorporated in ProUCL 4.0. 

It should be noted that the goodness-of-fit tests for gamma distribution depend upon the MLEs of gamma 

parameters, k and θ, which should be computed first before performing the goodness-of-fit tests. It is 

noted that the information about estimation of gamma parameters, gamma GOF tests, and construction of 

gamma Q-Q plots is not easily available in statistical textbooks. Therefore, the detailed description of 

these methods for gamma distribution is provided as follows. 

2.2.2.1 Quantile-Quantile (Q-Q) Plot for a Gamma Distribution 

Let x1, x2, ... , xn be a random sample from the gamma distribution, G(k,θ). Let x(1) ≤ x(2) ≤ ... ≤ x(n) 

represent the ordered sample. Let k

ˆ and θ

ˆ represent the maximum likelihood estimates (MLEs) of k and 

θ, respectively. For details of the computation of the MLEs of k and θ, refer to Singh, Singh, and Iaci 

(2002). Estimations of the gamma parameters are also briefly described later in this Technical Guide. The 

Q-Q plot for gamma distribution is obtained by plotting the scatter plot of pairs (x0i , x(i) ) i := 1, 2, ..., n. 

37 
Here the quantiles, x0i, are given by the equation, x0i = z0i

θ

ˆ / 2; i := 1, 2, ..., n, where the quantiles Z0i 

(already ordered) are obtained by using the inverse chi-square distribution and are given as follows. 

z0i 

∫ f (χ 2

2 

k

ˆ )dχ 2

2 

k

ˆ = (i −1/ 2) / n; i := 1, 2, ..., n (2-4) 

0 

2 In (2-4), χ 2k

ˆ represents a chi-square random variable with 2k

ˆ degrees of freedom (df). The program, 

PPCHI2 (Algorithm AS91) as given in Best and Roberts (1975), Applied Statistics (1975, Vol. 24, No. 3) 

has been used to compute the inverse chi-square percentage points, as given by the above equation given 

by (2-4). This represents an informal graphical method to test for a gamma distribution. All relevant 

statistics including the MLE of k are also displayed on the gamma Q-Q plot. 

This informal test should always be accompanied by the formal Anderson-Darling (A-D) test or 

Kolmogorov-Smirnov (K-S) test and vice versa. A linear pattern displayed by the scatter plot of bulk of 

the data may suggest an approximate gamma distribution. For example, a high value (e.g., 0.95 or greater) 

of the correlation coefficient of the linear pattern may suggest approximate gamma distribution of the data 

set under study. However, on this Q-Q plot points well separated from the bulk of data may represent 

outliers. Apparent breaks and jumps in the gamma Q-Q plot suggest the presence of multiple populations. 

Thus, Q-Q plots are also useful to identify outliers or the presence of multiple populations. The 

correlation coefficient of such a Q-Q plot (e.g., with outliers) can still be high which does not necessarily 

imply that the data follow a gamma distribution. Therefore, graphical Q-Q plot and other formal EDF 

tests, such as the Anderson-Darling (A-D) test or the Kolmogorov-Smirnov (K-S) test should be used on 

the same data set. A formal statistical test such as a K-S test or A-D test may lead to conclusion of a 

gamma distribution even for a data set with potential outliers and multiple populations. The final 

conclusion about the data distribution should be based upon the formal goodness-of-fit tests. This 

statement is true for all GOF tests (e.g., normal, lognormal, and gamma distributions) as incorporated in 

ProUCL 4.0. 

2.2.2.2 Empirical Distribution Function (EDF)-Based Goodness-of-Fit Tests 

Next, the two formal empirical distribution function (EDF)-based test statistics to test for a gamma 

distribution are briefly described here. Let F(x) be the cumulative distribution function (CDF) of the 

gamma random variable X. Let Z = F(X), then Z represents a uniform U(0,1) random variable. For each xi, 

compute zi by using the incomplete gamma function given by the equation zi = F (xi); i := 1, 2, ..., n. The 

algorithm as given in Numerical Recipes book (Press et al., 1990) has been used to compute the 

incomplete gamma function. Arrange the resulting zi in ascending order as z(1) ≤ z(2) ≤ ... ≤ z(n). Let 

z = ⎜

⎜

⎛

∑

n 

zi ⎟

⎟

⎞

/ n be the mean of the zi; i := 1, 2, ..., n. 

⎝ i=1 ⎠

Compute the following two test statistics. 

D+ = maxi {1/ n − z(i)}, and D− = maxi {z(i) − (i −1) / n} (2-5) 

The Kolmogorov-Smirnov test statistic is given by D = max(D+ , D− ) . 

38 
The Anderson-Darling test statistic is given by the following equation. 

n 

A2 = −n − (1/ n)∑{(2i −1)[log z(i) + log(1− z(n+1−i) )]} (2-6) 

i=1 

The critical values for these two statistics, D and A2

, are not readily available. For the Anderson-Darling 

test, only the asymptotic critical values are available in the statistical literature (D’Agostino and Stephens 

(1986)). Some raw critical values for K-S test are given in Schneider (1978), and Schneider and Clickner 

(1976). For these two tests, ExpertFit (2001) software and Law and Kelton (2000) use generic critical 

values for all completely specified distributions as given in D’Agostino and Stephens (1986). It is 

observed that the conclusions derived using these generic critical values for completely specified 

distributions and the simulated critical values for the gamma distribution with unknown parameters can be 

different. Therefore, to test for a gamma distribution, it is preferred and advised to use the critical values 

of these test statistics specifically obtained for gamma distributions with unknown parameters. 

In practice, the distributions are not completely specified and exact critical values for these two test 

statistics are needed. It should be noted that the distributions of the K-S test statistic, D, and the A-D test 

statistic, A2

, do not depend upon the scale parameter, θ; therefore, the scale parameter, θ, has been set 

equal to 1 in all of the simulation experiments. The critical values for these two statistics have been 

obtained via extensive Monte Carlo simulation experiments for several small and large values of the 

shape parameter, k, and with θ = 1. These critical are included in Appendix A. In order to generate the 

critical values, random samples from gamma distributions were generated using the algorithm as given in 

Whittaker (1974). It is observed that the critical values thus obtained are in close agreement with all 

available published critical values. The generated critical values for the two test statistics have been 

incorporated in ProUCL for three levels of significance, 0.1, 0.05, and 0.01. For each of the two tests, if 

the test statistic exceeds the corresponding critical value, then the hypothesis that the data follow a 

gamma distribution is rejected. ProUCL computes these test statistics and prints them on the gamma Q-Q 

plot and also on the UCL summary output sheets generated by ProUCL. 

2.3 Estimation of Parameters of the Three Distributions as Incorporated 

in ProUCL 

Throughout this Technical Guide, μ1 and σ1

2

 are the mean and variance of the random variable, X, and μ

and σ

2

 are the mean and variance of the random variable, Y = log(X). Also, σˆ represents the standard 

deviation of the log-transformed data. It should be noted that for both lognormal and gamma distributions, 

the associated random variable can take only positive values. This is typical of environmental data sets to 

consist of only positive values. 

2.3.1 Normal Distribution 

Let X be a continuous random variable (e.g., concentration of COPC), which follows a normal 

distribution, N (μ1, σ1

2

) with mean, μ1, and variance, σ1

2

. The probability density function of a normal 

distribution is given by the following equation: 

f (x; μ,σ) = 1 exp[−(x − μ)

2 / 2σ2 ]; − ∞ < x < ∞ (2-7)

2πσ

39 
For normally distributed data sets, it is well known (Hogg and Craig, 1978) that the minimum variance 

unbiased estimates (MVUEs) of the mean, μ1, and the variance, σ1

2

, are respectively given by the sample 

mean, x , and sample variance, sx 

2

. It is also well known that for normally distributed data sets, a UCL of 

the unknown mean, μ1, base upon Student’s t-distribution is optimal. It is observed via Monte Carlo 

simulation experiments (Singh and Singh (2003) Draft EPA Report) that for normally distributed data 

sets, the modified t-UCL and UCL based upon bootstrap t method also provide the exact 95% coverage to 

the population mean. For normally distributed data sets, the UCLs based upon these three methods are 

very similar. 

Lognormal Distribution 

If Y = log(X) is normally distributed with the mean, μ, and variance, σ

2

, then X is said to be lognormally 

distributed with parameters μ and σ

2

 and is denoted by LN(μ, σ

2

). It should be noted that μ and σ

2

 are not 

the mean and variance of the lognormal random variable, X, but they are the mean and variance of the 

log-transformed random variable, Y, whereas μ1, and σ1

2

 represent the mean and variance of X. Some 

parameters of interest of a two-parameter lognormal distribution, LN(μ, σ

2

), are given as follows: 

 Mean = μ1 = exp(μ + 0.5σ

2 ) (2-8) 

 Median = M = exp(μ) (2-9) 

Variance = σ1

2 = exp(2μ + σ2 )[exp(σ2

) −1] (2-10) 

 Coefficient of Variation = CV = σ1 = exp(σ

2

μ ) −1 (2-11) 1 

 Coefficient of Skewness = CV3

+ 3CV (2-12) 

2.3.2.1 MLEs of the Parameters of a Lognormal Distribution 

For lognormal distributions, note that y and sy (= σˆ ) are the maximum likelihood estimators (MLEs) of μ

and σ, respectively. The MLE of any function of the parameters μ and σ

2

 is obtained by simply 

substituting these MLEs in place of the parameters (Hogg and Craig 1978). Therefore, replacing μ and σ

by their MLEs in equations (2-8) through (2-12) will result in the MLEs (but biased) of the respective 

parameters of the lognormal distribution. The program ProUCL computes all of these MLEs for 

lognormally distributed data sets. These MLEs are printed on the Excel-type output spreadsheet generated 

by ProUCL. 

2.3.2.2 Relationship between Skewness and Standard Deviation, σ

Note that for a lognormal distribution, the CV (given by equation (2-11) above) and the coefficient of 

skewness (given by equation (2-12)) depend only on σ. Therefore, in this Technical Guide and also in 

ProUCL, the standard deviation, σ (Sd of log-transformed variable, Y), or its MLE, sy (= σˆ ), has been used 

as a measure of the skewness of lognormal and also of other skewed data sets with positive values. The 

larger is the Sd, the larger are the CV and the skewness. For example, for a lognormal distribution: with σ

= 0.5, the skewness = 1.75; with σ =1.0, the skewness = 6.185; with σ =1.5, the skewness = 33.468; and 

with σ = 2.0, the skewness = 414.36. Thus, the skewness of a lognormal distribution becomes 

40 
unreasonably large as σ starts approaching and exceeding 2.0. Note that for a gamma distribution, the 

skewness is a function of the shape parameter, k. As k decreases, the skewness increases. 

It is observed (Singh, Singh, Engelhardt (1997) and Singh, Singh, and Iaci (2002b)) that for smaller 

sample sizes (such as smaller than 50), and for values of σ approaching 2.0 (and skewness approaching 

414), the use of the H-statistic-based UCL results in impractical and unacceptably large values. For 

simplicity, the various levels of skewness of a positive data set as used in ProUCL and in this Technical 

Guide are summarized as follows: 

Table 2-1. Skewness as a Function of σ (or its MLE, sy = σˆ ), sd of log(X) 

Standard Deviation Skewness 

σ < 0.5 Symmetric to mild skewness 

0.5 ≤ σ < 1.0 Mild skewness to moderate skewness 

1.0 ≤ σ < 1.5 Moderate skewness to high skewness 

1.5 ≤ σ < 2.0 High skewness 

2.0 ≤ σ < 3.0 Extremely high skewness 

σ ≤ 3.0 Provides poor coverage 

These values of σ (or its estimate, Sd of log-transformed data) are used to define the skewness levels of 

lognormal and skewed non-discernable data distributions, as used in Tables 2-2 and 2-3. 

2.3.2.3 MLEs of the Quantiles of a Lognormal Distribution 

For highly skewed (e.g., σ exceeding 1.5), lognormally distributed populations, the population mean, μ1, 

often exceeds the higher quantiles (e.g., 80%, 90%, 95%) of the distribution. Therefore, the computation 

of these quantiles is also of interest. This is especially true when one may want to use the MLEs of the 

higher order quantiles (e.g., 95%, 97.5%, etc.) as an estimate of the EPC term. The formulae to compute 

these quantiles are briefly described here. 

The p

th quantile (or 100 pth percentile), xp, of the distribution of a random variable, X, is defined by the 

probability statement, P(X ≤ xp ) = p. If zp is the p

th quantile of the standard normal random variable, Z, 

with P(Z ≤ zp ) = p, then the p

th quantile of a lognormal distribution is given by xp = exp(μ + zpσ). Thus 

the MLE of the p

th quantile is given by 

xˆ 

p = exp(μˆ + z p σˆ) (2-13) 

For example, on the average, 95% of the observations from a lognormal LN(μ, σ

2

) distribution would lie 

below exp(μ + 1.65σ). The 0.5th quantile of the standard normal distribution is z0.5 = 0, and the 0.5th 

quantile (or median) of a lognormal distribution is M = exp(μ), which is obviously smaller than the mean, 

μ1, as given by equation (1-8). Also, note that the mean, μ1, is greater than xp if and only if σ > 2zp. For 

example, when p = 0.80, zp = 0.845, μ1 exceeds x0.80, the 80th percentile if and only if σ > 1.69, and, 

similarly, the mean, μ1, will exceed the 95th percentile if and only if σ > 3.29. ProUCL computes the 

MLEs of the 50% (median), 90%, 95%, and 99% percentiles of lognormally distributed data sets. For 

lognormally distributed background data sets, a 95% or 99% percentile may be used as an estimate of the 

background threshold value; that is the background level contaminant concentration. 

41 
2.3.2.4 MVUEs of Parameters of a Lognormal Distribution 

Even though the sample AM, x , is an unbiased estimator of the population AM, μ1, it does not have the 

minimum variance (MV). The MV unbiased estimates (MVUEs) of μ1 and σ1

2

 of a lognormal distribution 

are given as follows: 

μˆ1 = exp( y)gn (s

2 

y / 2) (2-14) 

σˆ1

2 = exp(2y)[gn (2s2 

y ) − gn ((n − 2)s

2 

y /(n −1))] (2-15) 

The series expansion of the function gn(μ) is given in Bradu and Mundlak (1970), and Aitchison and 

Brown (1976). Tabulations of this function are also provided by Gilbert (1987). Bradu and Mundlak 

(1970) give the MVUE of the variance of the estimate, μˆ1 , 

σˆ 2(μˆ1 ) = exp(2y)[(gn (2s y 

2 )) 2 − gn ((n − 2)s2 

y /(n −1))] (2-16) 

The square root of the variance given by equation (1-16) is called the standard error (SE) of the estimate, 

μˆ1 , given by equation (2-14). Similarly, a MVUE of the median of a lognormal distribution is given by 

Mˆ = exp(y)gn [−s

2 

y /(2(n −1))] (2-17) 

For a lognormally distributed data set, ProUCL also computes these MVUEs given by equations (2-14) 

through (2-17). 

2.3.2 Estimation of the Parameters of a Gamma Distribution 

Next, we consider the estimation of parameters of a gamma distribution. Since the estimation of gamma 

parameters is typically not included in standard statistical textbooks, this has been described in some 

detail in this Technical Guide. The population mean and variance of a two-parameter gamma distribution, 

G(k, θ), are functions of both parameters, k and θ. In order to estimate the mean, one has to obtain 

estimates of k and θ. The computation of the maximum likelihood estimate (MLE) of k is quite complex 

and requires the computation of Digamma and Trigamma functions. Several authors (Choi and Wette, 

1969, Bowman and Shenton 1988, Johnson, Kotz, and Balakrishnan, 1994) have studied the estimation of 

the shape and scale parameters of a gamma distribution. The maximum likelihood estimation method to 

estimate the shape and scale parameters of a gamma distribution is described below. 

Let x1, x2, ... , xn be a random sample (e.g., representing contaminant concentrations) of size n from a 

gamma distribution, G(k, θ), with unknown shape and scale parameters, k and θ, respectively. The log 

likelihood function (obtained using equation (2-3)) is given as follows: 

LogL(x1 , x2 ,..., xn ; k,θ) = −nk log(θ) − n log Γ(k) + (k −1)∑log(xi ) −∑xi θ (2-18) 

To find the MLEs of k and θ, we differentiate the log likelihood function as given in (1-18) with respect 

to k and θ, and set the derivatives to zero. This results in the following two equations: 

42 
Log(θ

ˆ) +

Γ′(k

ˆ) = 1 ∑log(xi ) , and (2-19)

Γ(k

ˆ) n 

k

ˆ

θ

ˆ = 1 ∑xi = x (2-20)

n 

Solving equation (2-20) for θ

ˆ , and substituting the result in (2-19), we get following equation: 

Γ

Γ

′

(

( 

k

k 

ˆ

ˆ

)

)

− log(k

ˆ) = 1 

n ∑log(xi ) − log⎜

⎛

⎝

1 

n ∑x ⎟

⎞

⎠ (2-21) i 

There does not exist a closed form solution of equation (2-21). This equation needs to be solved 

numerically for k

ˆ , which requires the use of Digamma and Trigamma functions. This is quite easy to do 

using a personal computer. An estimate of k can be computed iteratively by using the Newton-Raphson 

(Faires and Burden 1993) method, leading to the following iterative equation: 

k

ˆ 

l = k

ˆ 

l−1 − log(

1

k

ˆ

/ 

l

k 

−

ˆ

1 

l−

)

1 

−

−

Ψ

Ψ

( 

′

k

ˆ

( 

l

k 

−

ˆ

1 

l−

)

1 

−

) 

M 

(2-22) 

The iterative process stops when k

ˆ starts to converge. In practice, convergence is typically achieved in 

fewer than 10 iterations. In equation (2-22), 

M = log(x) −∑log(xi ) n , Ψ(k) = d (log Γ(k)), and Ψ′(k) = d (Ψ(k)) dk dk 

Here Ψ(k) is the Digamma function and Ψ′(k) is the Trigamma function. In order to obtain the MLEs 

of k and θ, one needs to compute the Digamma and Trigamma functions. Good approximate values for 

these two functions (Choi and Wette 1969) can be obtained using the following approximations. For k ≥

8, these functions are approximated by 

2 2 Ψ(k) ≈ log(k) − {1+ [1− (1/10 −1/(21k )) k ] (6k)} (2k) , and (2-23) 

2 2 Ψ′(k) ≈ {1+ {1+ [1− (1/ 5 −1/(7k )) / k ] (3k)} (2k)} k (2-24) 

For k < 8, one can use the following recurrence relation to compute these functions: 

Ψ(k) = Ψ(k +1) −1/ k , and (2-25) 

Ψ′(k) = Ψ′(k +1) +1/ k 2 (2-26) 

In ProUCL, equations (2-23) - (2-26) have been used to estimate k. The iterative process requires an 

initial estimate of k. A good starting value for k in this iterative process is given by k0 = 1 / (2M). Thom 

(1968) suggested the following approximation as an estimate of k: 

43 
4 ⎞

k

ˆ ≈ 4

1 

M ⎝

⎜

⎜

⎛

1+ 1+ M ⎟

⎟ (2-27) 3 ⎠

Bowman and Shenton (1988) suggested using k

ˆ , as given by (2-27), to be a starting value of k for an 

iterative procedure, calculating k

ˆ 

l at the l

th iteration from the following formula: 

k

ˆ 

l = k

ˆ 

l−1{log(k

ˆ 

l −1 ) − Ψ(k

ˆ 

l−1 ) (2-28) M 

Both equations (2-22) and (2-28) have been used to compute the MLE of k. It is observed that the 

estimate, k

ˆ , based upon Newton-Raphson method, as given by equation (2-22), is in close agreement 

with that obtained using equation (2-28) with Thom’s approximation as an initial estimate. Choi and 

Wette (1969) further concluded that the MLE of k, k

ˆ , is biased high. A bias-corrected (Johnson, Kotz, 

and Balakrishnan 1994) estimate of k is given by: 

k

ˆ* = (n − 3)kˆ/ n + 2 /(3n) (2-29) 

In (2-29), k

ˆ is the MLE of k obtained using either (2-22) or (2-28). Substitution of equation (2-29) in 

equation (2-20) yields an estimate of the scale parameter, θ, given as follows: 

* * θ

ˆ = x / kˆ (2-30) 

ProUCL computes simple MLEs of k and θ, and also bias-corrected estimates of k and θ. The biascorrected estimate of k as given by (2-29) has been used in the computation of the UCLs (as given by 

equations (2-34) and (2-35)) of the mean of a gamma distribution. 

2.4 Methods for Computing a UCL of the Unknown Population Mean 

ProUCL computes a (1 – α)100% UCL of the population mean, μ1, using the following 5 parametric and 

10 nonparametric methods. Five of the 10 nonparametric methods are based upon the bootstrap method. 

Modified t and adjusted central limit theorem adjust for skewness for skewed data sets. However, it is 

noted that (Singh, Singh, and Iaci (2002b) and Singh and Singh (2003)) this adjustment is not adequate 

enough for moderately skewed to highly skewed data sets. For details, interested users are referred to 

graphical displays of coverage probability comparisons for normal, gamma, and lognormal distributions 

given in Singh and Singh (2003). 

Parametric Methods 

1. Student’s t-statistic – assumes normality or approximate normality 

2. Approximate gamma UCL – assumes gamma distribution of the data set 

3. Adjusted gamma UCL – assumes gamma distribution of the data set 

44 
4. Land’s H-Statistic – assumes lognormality 

5. Chebyshev Theorem using the MVUE of the parameters of a lognormal distribution 

(denoted by Chebyshev (MVUE)) – assumes lognormality 

Nonparametric Methods 

1. Modified t-statistic – modified for skewed distributions 

2. Central limit theorem (CLT) – to be used for large samples 

3. Adjusted central limit theorem (adjusted-CLT) – adjusted for skewed distributions and to 

be used for large samples 

4. Chebyshev Theorem using the sample arithmetic mean and Sd (denoted by Chebyshev 

(Mean, Sd)) 

5. Jackknife method – yields the same result as Student’s t-statistic for the UCL of the 

population mean 

6. Standard bootstrap 

7. Percentile bootstrap 

8. Bias-corrected accelerated (BCA) bootstrap 

9. Bootstrap t 

10. Hall’s bootstrap 

Even though it is well known that some of the methods (e.g., CLT, UCL based upon Jackknife method 

(same as Student’s t-UCL), standard bootstrap and percentile bootstrap methods) do not perform well 

enough to provide the adequate coverage to the population mean of skewed distributions, these methods 

have been included in ProUCL to satisfy the curiosity of all users. 

ProUCL can compute a (1 – α)100% UCL (except for the H-UCL and adjusted gamma UCL) of the mean 

for any confidence coefficient (1 – α) value lying in the interval [0.5, 1.0). For the computation of the HUCL, only two confidence levels, namely, 0.90 and 0.95 are supported by ProUCL. For adjusted gamma 

UCL, three confidence levels, namely: 0.90, 0.95, and 0.99 are supported by ProUCL 4.0. An 

approximate gamma UCL can be computed for any level of significance in the interval [0.5,1). Whenever, 

ProUCL 4.0 cannot compute a UCL for a specified confidence coefficient (e.g., 0.99 for H-UCL), 

ProUCL 4.0 prints out “N/A.” Based upon sample size, n, skewness, and data distribution, ProUCL 4.0 

also makes recommendations on how to obtain an appropriate 95% UCL of the unknown population 

mean, μ1. 

2.4.1 (1 – α)100% UCL of the Mean Based Upon Student’s t-Statistic 

The widely used well-known Student’s t-statistic is given by, 

45 
t = x − μ1 (2-31) 

s x / n 

where x and sx are, respectively, the sample mean and sample standard deviation obtained using the raw 

data. If the data are a random sample from a normal population with mean, μ1, and standard deviation, σ1, 

then the distribution of this statistic is the familiar Student’s t-distribution with (n -1) degrees of freedom 

(df). Let tα,n-1 be the upper αth quantile of the Student’s t-distribution with (n -1) df. 

A (1 – α)100% UCL of the population mean, μ1, is given by,

 UCL = x + tα,n−1s x / n (2-32) 

For a normally (when the skewness is about ~0) distributed population, equation (2-32) provides the best 

(optimal) way of computing a UCL of the mean. Equation (2-32) may also be used to compute a UCL of 

the mean based upon very mildly skewed (e.g., |skewness|<0.5) data sets, where the skewness is given by 

equation (2-43). It should be pointed out that even for mildly to moderately skewed data sets (e.g., when 

σ, the Sd of log-transformed data, starts approaching and exceeding 0.5), the UCL given by (2-32) might 

not provide the desired coverage (e.g., = 0.95) to the population mean. This is especially true when the 

sample size is smaller than 20-25 (Singh and Singh (2003)). The situation gets worse (coverage much 

smaller than 0.95) for higher values of the Sd, σ, or its MLE, sy. 

2.4.2 Computation of the UCL of the Mean of a Gamma, G (k, θ), Distribution 

In the statistical literature, even though methods exist to compute a UCL of the mean of a gamma 

distribution (Grice and Bain 1980, Wong 1993), those methods have not become popular due to their 

computational complexity. Those approximate and adjusted methods depend upon the chi-square 

distribution and an estimate of the shape parameter, k. As seen above, computation of an MLE of k is 

quite involved, and this works as a deterrent to the use of a gamma distribution-based UCL of the mean. 

However, the computation of a gamma UCL currently should not be a problem due to easy availability of 

personal computers. 

Given a random sample, x1, x2, ... , xn , of size n from a gamma, G(k, θ), distribution, it can be shown that 

2nx / θ follows a chi-square distribution, χ 2

2 

nk , with 2nk degrees of freedom (df). When the shape 

parameter, k, is known, a uniformly most powerful test of size of the null hypothesis, H0: μ1 ≥ Cs, against 

the alternative hypothesis, H1: μ1 < Cs, is to reject H0 if x / Cs < χ 2

2 

nk (α) 2nk . The corresponding (1 – α) 

100% uniformly most accurate UCL for the mean, μ1, is then given by the probability statement. 

P(2nkx χ 2

2 

nk (α) ≥ μ1 ) = 1− α (2-33) 

where χ υ

2 denotes the cumulative percentage point of the chi-square distribution (e.g., α is the area in the 

left tail). That is, if Y follows χ υ

2 , then P(Y ≤ χ υ2

(α)) = α . In practice, k is not known and needs to be 

estimated from data. A reasonable method is to replace k by its bias-corrected estimate, k

ˆ* , as given by 

equation (2-29). This yields the following approximate (1 – α)100% UCL of the mean, μ1. 

46 
2 Approximate – UCL = 2nk

ˆ* 

x χ (α) (2-34) 2nk

ˆ* 

It should be pointed out that the UCL given by equation (2-34) is an approximate UCL and there is no 

guarantee that the confidence level of (1 – α) will be achieved by this UCL. However, it does provide a 

way of computing a UCL of the mean of a gamma distribution. Simulation studies conducted in Singh, 

Singh, and Iaci (2002b) and in Singh and Singh (2003) suggest that an approximate gamma UCL thus 

obtained provides the specified coverage (95%) as the shape parameter, k, approaches 0.5. Therefore, 

when k ≥ 0.5, one can always use the approximate UCL given by equation (2-34) to estimate the EPC 

term. This approximation is good even for smaller (e.g., n = 5) sample sizes as shown in Singh, Singh, 

Iaci (2002b), and in Singh and Singh (2003). 

Grice and Bain (1980) computed an adjusted probability level, β (adjusted level of significance), which 

can be used in (2-34) to achieve the specified confidence level of (1 – α). For α = 0.05 (confidence 

coefficient of 0.95), α = 0.1, and α = 0.01, these probability levels are given below in Table 2-2 for some 

values of the sample size n. One can use interpolation to obtain an adjusted β for values of n not covered 

in the table. The adjusted (1 – α)100% UCL of the gamma mean, μ1 = kθ, is given by the following 

equation. 

2 Adjusted – UCL = 2nk

ˆ* 

x χ (β) (2-35) 2nk

ˆ* 

where β is given in Table 2-1 for α = 0.05, 0.1, and 0.01. Note that as the sample size, n, becomes large, 

the adjusted probability level, β, approaches the specified level of significance, α. Except for the 

computation of the MLE of k, equations (2-34) and (2-35) provide simple chi-square-distribution-based 

UCLs of the mean of a gamma distribution. It should also be noted that the UCLs as given by (2-34) and 

(2-35) only depend upon the estimate of the shape parameter, k, and are independent of the scale 

parameter, θ, and its ML estimate. Consequently, as expected, it is observed that coverage probabilities 

for the mean associated with these UCLs do not depend upon the values of the scale parameter, θ. It 

should also be noted that gamma UCLs do not depend upon the standard deviation of data which gets 

distorted by the presence of outliers. Thus, outliers will have reduced influence on the computation of the 

gamma distribution based upon UCLs of the mean, μ1. 

Table 2-2. Adjusted Level of Significance, β

α = 0.05 α = 0.1 α = 0.01 

n probability level, β probability level, β probability level, β

5 0.0086 0.0432 0.0000 

10 0.0267 0.0724 0.0015 

20 0.0380 0.0866 0.0046 

40 0.0440 0.0934 0.0070 

-- 0.0500 0.1000 0.0100 

2.4.3 (1 – α)100% UCL of the Mean Based Upon H-Statistic (H-UCL) 

The one-sided (1 – α)100% UCL for the mean, μ1, of a lognormal distribution as derived by Land (1971, 

1975) is given as follows: 

2 UCL = exp(y + 0.5s y + s yH1−α n −1) (2-36) 

47
Tables of H-statistic critical values can be found in Land (1975) and also in Gilbert (1987). Theoretically, 

when the population is lognormal, Land (1971) showed that the UCL given by equation (2-36) possesses 

optimal properties and is the uniformly most accurate unbiased confidence limit. However, it is noticed 

that, in practice, the H-statistic-based results can be quite disappointing and misleading, especially when 

the data set consists of outliers, or is a mixture from two or more distributions (Singh, Singh, and 

Engelhardt, 1997, 1999 and Singh, Singh, and Iaci, 2002b). Even a minor increase in the Sd, sy, 

drastically inflates the MVUE of μ1 and the associated H-UCL. The presence of low as well as high data 

values increases the Sd, sy, which in turn inflates the H-UCL. Furthermore, it is observed (Singh, Singh, 

Engelhardt, and Nocerino 2002a) that for samples of sizes smaller than 15-25, and for values of σ

approaching 1.0 and higher (for moderately skewed to highly skewed data sets), the use of H-statisticbased UCL results in impractical and unacceptably large UCL values. 

In practice, many data sets follow a lognormal as well as gamma model. However, the population mean 

based upon a lognormal model can be significantly greater (often unrealistically large) than the population 

mean based upon a gamma model. In order to provide the specified 95% coverage for an inflated mean 

based upon a lognormal model, the resulting UCL based upon H-statistic also yield impractical UCL 

values. The use of a gamma model results in practical estimates (e.g., UCL) of the population mean. 

Therefore, for positively skewed data sets, it is recommended to test for a gamma model first. If data 

follow a gamma distribution, then the UCL of the mean should be computed using a gamma distribution. 

The gamma distribution is better suited to model positively skewed environmental data sets. 

2.4.4 (1 – α)100% UCL of the Mean Based Upon Modified t-Statistic for Asymmetrical 

Populations 

Chen (1995), Johnson (1978), Kleijnen, Kloppenburg, and Meeuwsen (1986), and Sutton (1993) 

suggested the use of the modified t-statistic for testing the mean of a positively skewed distribution 

(including the lognormal distribution). The (1 – α)100% UCL of the mean thus obtained is given by 

UCL = x + μˆ 3 (6s x 

2 

n) + tα,n−1s x n (2-37) 

Where μˆ 3 , an unbiased moment estimate (Kleijnen, Kloppenburg, and Meeuwsen 1986) of the third 

central moment, is given as follows, 

n 

μˆ 3 = n∑(xi − x)

3 (n −1)(n − 2) (2-38) 

i=1 

It should be pointed out that this modification for a skewed distribution does not perform well even for 

mildly to moderately skewed data sets (e.g., when σ starts approaching and exceeding 0.75). Specifically, 

it is observed that the UCL given by equation (2-37) may not provide the desired coverage of the 

population mean, μ1, when σ starts approaching and exceeding 0.75 (Singh, Singh, and Iaci, 2002b). This 

is especially true when the sample size is smaller than 20-25. This small sample size requirement 

increases as σ increases. For example, when σ starts approaching and exceeding 1.5, the UCL given by 

equation (2-37) does not provide the specified coverage (e.g., 95%), even for samples as large as 100. 

Since this method does not require any distributional assumptions, it is a nonparametric method. 

48 
2.4.5 (1 – α)100% UCL of the Mean Based Upon the Central Limit Theorem 

The central limit theorem (CLT) states that the asymptotic distribution, as n approaches infinity, of the 

sample mean, xn , is normally distributed with mean, μ1, and variance, σ1

2

/n. More precisely, the sequence 

of random variables given by 

σ / n 

xn − μ1 z = (2-39) n 

has a standard normal limiting distribution. In practice, for large sample sizes, n, the sample mean, x , has 

an approximate normal distribution irrespective of the underlying distribution function. Since the CLT 

method requires no distributional assumptions, this is a nonparametric method. 

As noted by Hogg and Craig (1978), if σ1 is replaced by the sample standard deviation, sx, the normal 

approximation for large n is still valid. This leads to the following approximate large sample 

nonparametric (1 – α)100% UCL of the mean, 

UCL = x + zα s x / n (2-40) 

An often cited and used rule of thumb for a sample size associated with the CLT method is n ≥ 30. 

However, this may not be adequate enough if the population is skewed, specifically when σ (Sd of logtransformed variable) starts exceeding 0.5 (Singh, Singh, Iaci, 2002b). In practice, for skewed data sets, 

even a sample as large as 100 is not large enough to provide adequate coverage to the mean of skewed 

populations (even for mildly skewed populations). A refinement of the CLT approach, which makes an 

adjustment for skewness by Chen (1995), is given as follows. 

2.4.6 (1 – α)100% UCL of the Mean Based Upon the Adjusted Central Limit Theorem 

(Adjusted-CLT) 

The “adjusted-CLT” UCL is obtained if the standard normal quantile, zα, in the upper limit of equation (2­

40) is replaced by (Chen, 1995) 

zα,adj = zα +

k

ˆ

3 (1+ 2zα

2 ) (2-41)

6 n 

Thus, the adjusted (1 – α)100% UCL for the mean, μ1, is given by 

2 UCL = x + [z + k

ˆ (1+ 2z ) (6 n)]s n (2-42) α 3 α x 

Here k

ˆ

3 , the coefficient of skewness (raw data), is given by

s 3 Skewness (raw data) k (2-43) ˆ

3 = μˆ 3 x 

49 
where μˆ 3 , an unbiased estimate of the third moment, is given by equation (2-38). This is another large 

sample approximation for the UCL of the mean of skewed distributions. This is a nonparametric method, 

as it does not depend upon any of the distributional assumptions. 

As with the modified t-UCL, it is observed that this adjusted-CLT UCL does not provide adequate 

coverage to the population mean when the population is skewed, specifically when σ starts approaching 

and exceeding 0.75 (Singh, Singh, and Iaci, 2002b, and Singh and Singh, 2003). This is especially true 

when the sample size is smaller than 20-25. This small sample size requirement increases as σ increases. 

For example, when σ starts approaching and exceeding 1.5, the UCL given by equation (2-42) does not 

provide the specified coverage (e.g., 95%), even for samples as large as 100. It is noted that UCL as given 

by (2-42) does not provide adequate coverage to the mean of a gamma distribution, especially when k ≤

1.0 and the sample size is small. 

Thus, the UCLs based upon these skewness adjusted methods, such as the Johnson’s modified t and 

Chen’s adjusted-CLT, do not provide the specified coverage to the population mean for mildly to 

moderately skewed (e.g., σ in (0.5, 1.0)) data sets, even for samples as large as 100 (Singh, Singh, and 

Iaci, 2002b). The coverage of the population mean provided by these UCLs becomes worse (much 

smaller than the specified coverage) for highly skewed data sets. 

2.4.7 Chebyshev (1 – α)100% UCL of the Mean Using Sample Mean and Sample sd 

The Chebyshev inequality can be used to obtain a reasonably conservative but stable estimate of the UCL 

of the mean, μ1. The two-sided Chebyshev theorem (Hogg and Craig, 1978) states that given a random 

variable, X, with finite mean and standard deviation, μ1 and σ1, we have 

P(−kσ1 ≤ x − μ1 ≤ kσ1 ) ≥ 1−1/ k 2 (2-44) 

This result can be applied on the sample mean, x (with mean, μ1 and variance, σ1

2 

n ), to obtain a 

conservative UCL for the population mean, μ1. For example, if the right side of equation (2-44) is equated 

to 0.95, then k = 4.47, and UCL = x + 4.47σ1 / n is a conservative 95% upper confidence limit for the 

population mean, μ1. Of course, this would require the user to know the value of σ1. The obvious 

modification would be to replace σ1 with the sample standard deviation, sx, but since this is estimated 

from data, the result is no longer guaranteed to be conservative. In general, the following equation can be 

used to obtain a (1 – α)100% UCL of the population mean, μ1: 

UCL = x + (1/ α)s x n (2-45) 

A slight refinement of equation (2-45) is given (suggested by S. Ferson) as follows, 

UCL = x + ((1/ α) −1)s x n (2-46) 

ProUCL computes the Chebyshev (1 – α)100% UCL of the population mean using equation (2-46). This 

UCL is labeled as Chebyshev (Mean, Sd) on the output sheets generated by ProUCL. Since this 

Chebyshev method requires no distributional assumptions about the data set under study, this is a 

nonparametric method. This UCL may be used as an estimate of the upper confidence limit of the 

50 
population mean, μ1, when the data are not normal, lognormal, or gamma distributed, especially when Sd, 

σ (or its estimate, sy), starts approaching and exceeding 1.5. 

2.4.8 Chebyshev (1 – α)100% UCL of the Mean of a Lognormal Population Using the MVUE 

of the Mean and its Standard Error 

ProUCL uses equation (2-44) on the MVUEs of the lognormal mean and Sd to compute a UCL (denoted 

by (1 – α)100% Chebyshev (MVUE)) of the population mean of a lognormal population. In general, if μ1 

is an unknown mean, μˆ1 is an estimate, and σˆ1 (μˆ1 ) is an estimate of the standard error of μˆ1 , then the 

following equation, 

((1/ α) −1)σˆ1 (μˆ1 UCL = μˆ ) (2-47) 1 +

yields an approximate (1 – α)100% UCL for μ1, which should tend to be conservative, but this is not 

assured. For example, for a lognormally distributed data set, a 95% (with α = 0.05) Chebyshev (MVUE) 

UCL of the mean can be obtained using the following equation, 

UCL = μˆ1 + 4.359σˆ1 (μˆ1 ) (2-48) 

Here μˆ1 and σˆ1 (μˆ1 ) are given by equations (2-14) and (2-16), respectively. Thus, for lognormally 

distributed data sets, ProUCL also uses equation (2-48) to compute a (1 – α)100% Chebyshev (MVUE) 

UCL of the mean. It should be noted that for lognormally distributed data sets, some recommendations to 

compute a 95% UCL of the population mean are summarized later in this chapter. It is recommended that 

for skewed data sets, one should always perform gamma goodness-of-fit (GOF) test. Many times, a 

skewed data set can be modeled both by a lognormal distribution as well as a gamma distribution. 

However, since, the use of a lognormal distribution often yields inflated and unstable upper limits 

including UCLs (Singh, Singh, and Engelhardt, 1997) and UPLs (Gibbons, 1994), it is suggested that if a 

data set follows a gamma distribution (even when data may also be lognormally distributed), then the 

UCL of mean, μ1 (and other upper limits) should be computed using a gamma distribution. This is 

especially true when the data are highly skewed with sd of log-transformed data exceeding 1.5, 2.0, and 

the sample size is small such as < 50, < 70, < 100. 

On the other hand, it is also noticed that the use of a lognormal distribution based H-UCL (based upon 

Land’s H-statistic) often yields a UCL that is lower than the sample mean. This is especially true for 

mildly skewed to moderately skewed data sets of larger sizes (e.g., >50, 100). Some examples illustrating 

this issue are given in Chapter 3 of the revised background document for CERCLA sites (EPA, 2002). 

Therefore, it is suggested to avoid the use of a lognormal distribution to model environmental data sets. 

From the Monte-Carlo results discussed in Singh, Singh, and Iaci (2002b), and in Singh and Singh 

(2003), it is observed that for highly skewed gamma distributed data sets (with k < 0.5), the coverage 

provided by the Chebyshev 95% UCL (given by (2-46)) is smaller than the specified coverage of 0.95. 

This is especially true when the sample size is smaller than 10-20. As expected, for larger samples sizes, 

the coverage provided by the 95% Chebyshev UCL is at least 95%. For larger samples, the Chebyshev 

95% UCL will result in a higher (but stable) UCL of the mean of positively skewed gamma distributions. 

It is observed (Singh and Singh 2003) that for moderately skewed to highly skewed lognormally 

distributed data sets (e.g., with σ exceeding 1), 95% Chebyshev MVUE UCL does not provide the 

specified coverage to the population mean. This is true when the sample size is less than 10-50. The 

51 
details and graphical displays can be found in Singh and Singh (2003). For highly skewed (e.g., σ > 2), 

lognormal data sets of sizes, n less than 50-70, the H-UCL results in unstable (impractical values which 

are orders of magnitude higher than other UCLs) unjustifiably large UCL values (Singh et al. 2002a). For 

such highly skewed lognormally distributed data sets of sizes less than 50-70, one may want to use 97.5% 

or 99% Chebyshev MVUE UCL of the mean as an estimate of the EPC term (Singh and Singh 2003). It 

should also be noted that for skewed data sets, the coverage provided by a 95% UCL based upon 

Chebyshev inequality is higher than those based upon the percentile bootstrap method or the BCA 

bootstrap method. Thus for skewed data sets, the Chebyshev inequality-based 95% UCL of the mean 

(samples of all sizes from both lognormal and gamma distributions) performs better than the 95% UCL 

based upon the BCA bootstrap method. Also, when data are lognormally distributed, the coverage 

provided by Chebyshev MVUE UCL (Singh and Singh 2003) is better than the one based upon Hall’s 

bootstrap or bootstrap t method. This is especially true when the sample size starts exceeding 10-15. 

However, for highly skewed data sets of sizes less than 10-15, it is noted that Hall’s bootstrap method 

provides slightly better coverage than the Chebyshev MVUE UCL method. Just as for the gamma 

distribution, it is observed that for lognormally distributed data sets, the coverage provided by Hall’s and 

bootstrap t methods do not increase much with the sample size. 

2.4.9 (1 – α)100% UCL of the Mean Using the Jackknife and Bootstrap Methods 

Bootstrap and jackknife methods as discussed by Efron (1982) are nonparametric statistical resampling 

techniques which can be used to reduce the bias of point estimates and construct approximate confidence 

intervals for parameters, such as the population mean. These two methods require no assumptions 

regarding the statistical distribution (e.g., normal, lognormal, or gamma) of the underlying population, 

and can be applied to a variety of situations no matter how complicated. There exists in the literature of 

statistics an extensive array of different bootstrap methods for constructing confidence intervals for the 

population mean, μ1. In the ProUCL 4.0 software package, five bootstrap methods have been 

incorporated: 

1. The standard bootstrap method, 

2. Bootstrap t method (Efron, 1982 and Hall, 1988), 

3. Hall’s bootstrap method (Hall, 1992 and Manly, 1997), 

4. Simple bootstrap percentile method (Manly, 1997), and 

5. Bias-corrected accelerated (BCA) percentile bootstrap method (Efron and Tibshirani, 

1993 and Many, 1997). 

Let x1, x2, ... , xn be a random sample of size n from a population with an unknown parameter, θ

(e.g., θ = μ1), and let θ

ˆ be an estimate of θ, which is a function of all n observations. For example, the 

parameter, θ, could be the population mean and a reasonable choice for the estimate, θ

ˆ , might be the 

sample mean, x . Another choice for θ

ˆ is the MVUE of the mean of a lognormal population, especially 

when dealing with lognormal data sets. 

52 
2.4.9.1 (1 – α)100% UCL of the Mean Based Upon the Jackknife Method 

In the jackknife approach, n estimates of θ are computed by deleting one observation at a time 

(Dudewicz and Misra 1988). Specifically, for each index, i, denote by θ

ˆ

(i) , the estimate of θ (computed 

similarly as θ

ˆ ) when the i

th observation is omitted from the original sample of size n, and let the 

arithmetic mean of these estimates be given by: 

θ

~ = 1 ∑

n 

θ

ˆ

(i) (2-49) 

n i=1 

A quantity known as the i

th "pseudo-value" is defined by 

Ji = nθ

ˆ − (n −1)θˆ

(i) (2-50) 

The jackknife estimator of θ is given by the following equation. 

J (θ

ˆ) = 1 ∑

n 

Ji = nθ

ˆ − (n −1)θ

~ (2-51)

n i=1 

If the original estimate θ

ˆ is biased, then under certain conditions, part of the bias is removed by the 

jackknife method, and an estimate of the SE of the jackknife estimate, J (θ

ˆ) , is given by 

σ ˆ ˆ 

J θ

ˆ = 1 n 

Ji − J θ

2 

∑( ( )) (2-52) ( ) nn ( −1) i=1 

Next, consider the t-type statistic given by 

J (θ

ˆ) −θ

t = (2-53) 

σˆ 

J (θ

ˆ) 

The t-type statistic given above has an approximate Student’s t- distribution with n – 1 degrees of 

freedom, which can be used to derive the following approximate (1–α) 100% UCL for θ, 

 UCL = J (θ

ˆ) + tα ,n−1σˆ 

J (θ

ˆ) (2-54) 

If the sample size, n, is large, then the upper αth t-quantile in the above equation can be replaced with the 

corresponding upper αth standard normal quantile, zα. Observe, also, that when θ

ˆ is the sample mean, x , 

then the jackknife estimate is also the sample mean, J (x) = x , and the estimate of the standard error 

given by equation (2-52) simplifies to sx/n

1/2

, and the UCL in equation (2-54) reduces to the familiar tstatistic based UCL given by equation (2-32). ProUCL uses the jackknife estimate as the sample mean, 

that yields J (x) = x , which in turn translates equation (2-54) to Student’s t- UCL given by equation (2­

53 
32). This method has been included in ProUCL to satisfy the curiosity of those users who do not 

recognize that this jackknife method (with sample mean as the estimator) yields a UCL of the population 

mean identical to the UCL based upon the Student’s t- statistic as given by equation (2-32). 

Note: It is well known that the Jackknife method (with sample mean as an estimator) and Student’s tmethod yield identical UCL values. However, a typical user may be unaware of this fact, and some 

researchers may want to see these issues described and discussed at one place. It is also noted that it has 

been suggested that a 95% UCL based upon the Jackknife method on the full data set obtained using 

robust ROS may provide adequate coverage (e.g., Shumway, Kayhanian, and Azari (2002)) to the 

population mean of skewed distributions, which of course is not true. It is well known (Singh, Singh, and 

Nocerino, 2003) that Student’s t-UCL (and therefore, Jackknife UCL) fails to provide adequate coverage 

to the population mean of moderate to highly skewed distributions. 

2.4.9.2 (1 – α)100% UCL of the Mean Based Upon the Standard Bootstrap Method 

In bootstrap resampling methods, repeated samples of size n are drawn with replacement from a given set 

of observations. The process is repeated a large number of times (e.g., 2000 times), and each time an 

estimate, θ

ˆ 

i , of θ is computed. The estimates thus obtained are used to compute an estimate of the SE of 

θ

ˆ . A description of the bootstrap method, illustrated by application to the population mean, μ1, and the 

sample mean, x , is given as follows. 

Step 1. Let (xi1, xi2, ... , xin) represent the i

th sample of size n with replacement from the original 

data set, (x1, x2, ..., xn). Then compute the sample mean and denote it by xi . 

Step 2. Repeat Step 1 independently N times (e.g., 1000-2000), each time calculating a new 

estimate. Denote these estimates (KM means, RMLE means) by x1 , x2 , ..., xN . The bootstrap 

estimate of the population mean is the arithmetic mean, xB , of the N estimates xi : i := 1, 2, ..., 

N. The bootstrap estimate of the standard error of the estimate, x , is given by: 

1 N 

σˆ B = ∑(xi − xB )

2 (2-55) N −1 i=1 

If some parameter, θ (say, the population median), other than the mean is of concern with an associated 

estimate (e.g., the sample median), then the same steps described above could be applied with the 

parameter and its estimates used in place of μ1 and x . Specifically, the estimate, θ

ˆ 

i , would be computed, 

instead of xi , for each of the N bootstrap samples. The general bootstrap estimate, denoted byθ B , is the 

arithmetic mean of the N estimates. The difference, θ B −θ

ˆ , provides an estimate of the bias of the 

estimate, θ

ˆ , and an estimate of the SE of θˆ is given by 

1 N 

σˆ B = ∑(θ

ˆ 

i − θB )

2 (2-56) N −1 i=1 

54 
A (1–α)100% standard bootstrap UCL for θ is given by 

UCL = θ

ˆ + zασˆ B (2-57) 

ProUCL computes the standard bootstrap UCL by using the population AM and sample AM, respectively 

given by μ1 and x . It is observed that the UCL obtained using the standard bootstrap method is quite 

similar to the UCL obtained using the Student’s t-statistic as given by equation (2-32), and, as such, does 

not adequately adjust for skewness. For skewed data sets, the coverage provided by standard bootstrap 

UCL is much lower than the specified coverage. 

2.4.9.3 (1 – α)100% UCL of the Mean Based Upon the Simple Percentile Bootstrap Method 

Bootstrap resampling of the original data set is used to generate the bootstrap distribution of the unknown 

population mean (Manly 1997). In this method, xi , the sample mean, is computed from the i

th resampling 

(i =1,2,..., N) of the original data. These xi ;, i:=1,2,...,N are arranged in ascending order 

as x(1) ≤ x(2) ≤ L ≤ x( N ) . The (1 – α)100% UCL of the population mean, μ1, is given by the value that 

exceeds the (1 – α)*100% of the generated mean values. The 95% UCL of the mean is the 95th percentile 

of the generated means and is given by: 

95% Percentile – UCL = 95th% xi ; i: = 1, 2, ..., N (2-58) 

For example, when N = 1000, a simple bootstrap 95% percentile-UCL is given by the 950th ordered mean 

value given by x(950) . 

Singh and Singh (2003) observed that for skewed data sets, the coverage provided by this simple 

percentile bootstrap method is much lower than the coverage provided by the bootstrap t and Hall’s 

bootstrap methods. It is observed that for skewed (lognormal and gamma) data sets, the BCA bootstrap 

method performs slightly better (in terms of coverage probability) than the simple percentile method. 

2.4.9.4 (1 – α)100% UCL of the Mean Based Upon the Bias-Corrected Accelerated (BCA) 

Percentile Bootstrap Method 

The BCA bootstrap method is also a percentile bootstrap method adjusts for bias in the estimate (Efron 

and Tibshirani 1993 and Manly 1997). The performance of this method for skewed distributions (e.g., 

lognormal and gamma) is not well studied. It was conjectured that the BCA method would perform better 

than the various other methods. Singh and Singh (2003) investigated and compared its performance (in 

terms of coverage probabilities) with parametric methods and other bootstrap methods. For skewed data 

sets, this method does represent a slight improvement (in terms of coverage probability) over the simple 

percentile method. However, this improvement is not adequate enough and yields UCLs with a coverage 

probability much lower than the specified coverage of 0.95. The BCA upper confidence limit of intended 

(1 – α)100% coverage is given by the following equation: 

BCA – UCL = x (α2 ) (2-59) 

Here x (α2 ) is the α2100th percentile of the distribution of the xi ; i: = 1, 2, ..., N. For example, when N = 

2000, x (α2 )

= (α2N)th ordered statistic of xi ; i: = 1, 2, ..., N given by x(α2 N ) . 

55 
Here α2 is given by the following probability statement: 

⎡ zˆ + z(1−α ) ⎤

α 2 = Φ⎢zˆ0 + 0 ⎥ (2-60) 

⎣ 1−αˆ(zˆ0 + z(1−α )

)⎦

Here Φ(.) is the standard normal cumulative distribution function and z(1 – α)

 is the 100(1–α)

th percentile of 

a standard normal distribution. For example, z(0.95) = 1.645, and Φ(1.645) = 0.95. Also in the equation (2­

60), zˆ0 (bias correction) and αˆ (acceleration factor) are given by 

zˆ0 = Φ−1 ⎢

⎡#(xi < x)

⎥

⎤ (2-61) ⎣ N ⎦

Here Φ-1 (.) is the inverse function of a standard normal cumulative distribution function, e.g., Φ-1 (0.95) 

= 1.645. αˆ is the acceleration factor and is given by the following equation. 

∑(x − x−i )

3 

αˆ = 6[∑(x − x−i )

2 ]1.5 (2-62) 

Here the summation in (2-62) is being carried from i = 1 to i = n, the sample size. x is the sample mean 

based upon all n observation and x−i is the mean of (n-1) observations without the i

th observation, i: = 1, 

2, ..., n. 

Singh and Singh (2003) observed that for skewed data sets (e.g., gamma and lognormal), the coverage 

provided by this BCA percentile method is much lower than the coverage provided by the bootstrap t and 

Hall’s bootstrap methods. This is especially true when the sample size is small. The BCA method does 

provide an improvement over the simple percentile method and the standard bootstrap method. However, 

bootstrap t and Hall’s bootstrap methods perform better (in terms of coverage probabilities) than the BCA 

method. For skewed data sets, the BCA method also performs better than the modified t-UCL. For gamma 

distributions, the coverage provided by BCA 95% UCL approaches 0.95 as the sample size increases. For 

lognormal distributions, the coverage provided by the BCA 95% UCL is much lower than the specified 

coverage of 0.95. 

2.4.9.5 (1 – α)100% UCL of the Mean Based Upon the Bootstrap t Method 

Another variation of the bootstrap method, called the “bootstrap t” by Efron (1982), is a nonparametric 

method that uses the bootstrap methodology to estimate quantiles of the pivotal quantity, t statistic, given 

by equation (2-31). Rather than using the quantiles of the familiar Student’s t-statistic, Hall (1988) 

proposed to compute estimates of the quantiles of the statistic given by equation (2-31) directly from the 

data. 

Specifically, in Steps 1 and 2 described above in Section 2.4.9.2, if x is the sample mean computed from 

the original data, and xi and sx,I are the sample mean and sample standard deviation computed from the i

th 

resampling of the original data, the N quantities ti = n[(xi − x)/ s x,i ] are computed and sorted, 

yielding ordered quantities, t(1) ≤ t(2) ≤ ... ≤ t(N). The estimate of the lower αth quantile of the pivotal 

56 
quantity in equation (2-31) is tα, B = t(αN). For example, if N = 1000 bootstrap samples are generated, then 

the 50th ordered value, t(50) , would be the bootstrap estimate of the lower 0.05th quantile of the pivotal 

quantity in equation (2-31). Then a (1–α)100% UCL of the mean based upon the bootstrap t-method is 

given as follows. 

s

UCL = x − t(αN ) 

x (2-63) 

n 

Note the “ – ” sign in equation (2-63). ProUCL computes the bootstrap t UCL based upon the quantiles 

obtained using the sample mean, x . It is observed that the UCL based upon the bootstrap t method is 

more conservative than the other UCLs obtained using the Student’s t, modified-t, adjusted-CLT, and the 

standard bootstrap methods. This is especially true for skewed data sets. This method seems to adjust for 

skewness to some extent. 

It is observed that for skewed data sets (e.g., gamma, lognormal), the 95% UCL based upon the bootstrap 

t method performs better than the 95% UCLs based upon the simple percentile and the BCA percentile 

methods (Singh and Singh (2003)). For highly skewed (k < 0.1 or σ > 2.5-3.0) data sets of small sizes 

(e.g., n < 10), the bootstrap t method performs better than other (adjusted gamma UCL, or Chebyshev 

inequality UCL) UCL computation methods. It is noted that for the gamma distribution, the performances 

(coverages provided by the respective UCLs) of the bootstrap t and Hall’s bootstrap methods are very 

similar. It is also noted that for larger samples, these two methods (bootstrap t and Hall’s bootstrap) 

approximately provide the specified 95% coverage to the mean, kθ, of the gamma distribution. For 

gamma distributed data sets, the coverage provided by a bootstrap t (and Hall’s bootstrap UCL) 95% UCL 

approaches 95% as sample size increases for all values of k considered (k = 0.05-5.0) in Singh and Singh 

(2003). However, it is noted that the coverage provided by these two bootstrap methods is slightly lower 

than 0.95 for samples of smaller sizes. 

For lognormally distributed data sets, the coverage provided by the bootstrap t 95% UCL is a little bit 

lower than the coverage provided by the 95% UCL based upon Hall’s bootstrap method. However, it 

should be noted that for lognormally distributed data sets, for samples of all sizes, the coverage provided 

by these two methods (bootstrap t and Hall’s bootstrap) is significantly lower than the specified 0.95 

coverage. This is especially true for moderately skewed to highly skewed (e.g., σ >1.0) lognormally 

distributed data sets. It should be pointed out that the bootstrap t and Hall’s bootstrap methods sometimes 

result in unstable, erratic, and unreasonably inflated UCL values especially in the presence of outliers 

(Efron and Tibshirani, 1993). Therefore, these two methods should be used with caution. If this is the 

case, and these two methods result in erratic and inflated UCL values, then an appropriate Chebyshev 

inequality based UCL may be used to estimate the EPC term for nonparametric skewed data sets. 

2.4.9.6 (1 – α)100% UCL of the Mean Based Upon Hall’s Bootstrap Method 

Hall (1992) proposed a bootstrap method that adjusts for bias as well as skewness. This method has been 

included in UCL guidance document (EPA 2002a). For highly skewed data sets (e.g., LN (5,4)), it 

performs slightly better (higher coverage) than the bootstrap t method. In this method, xi and sx,i , 

and k

ˆ

3i , the sample mean, the sample standard deviation, and the sample skewness, respectively, are 

computed from the i

th resampling (i = 1, 2,..., N) of the original data. Let x be the sample mean, sx be the 

sample standard deviation, and k

ˆ

3 be the sample skewness (as given by equation (2-43)) computed from 

the original data. The quantities, Wi and Qi, given as follows are computed for each of the N bootstrap 

samples: 

57 
2 2 3 Wi = (xi − x) s x,i , and Qi (Wi ) = Wi + k

ˆ

3i

Wi / 3 + k

ˆ

3i

Wi / 27 + k

ˆ

3i /(6n) 

The quantities, Qi(Wi ) , given above are arranged in ascending order. For a specified (1 – α) confidence 

coefficient, compute the (αN)

th ordered value, qα , of the quantities, Qi(Wi ) . Next, compute 

W (qα ) using the inverse function, which is given as follows: 

⎛ 1/ 3 

W ⎞ (qα ) = 3⎜

⎝

(1+ k

ˆ

3 (qα − k

ˆ

3 /(6n))) −1⎟

⎠

/ k

ˆ

3 (2-64) 

In equation (2-64), k

ˆ

3 is computed using equation (2-43). Finally, the (1 – α)100% UCL of the population 

mean based upon Hall’s bootstrap method (Manly 1997) is given as follows: 

UCL = x −W (qα )s x (2-65) 

For gamma distributions, Singh and Singh (2003) observed that the coverage probabilities provided by 

the 95% UCLs based upon bootstrap t and Hall’s bootstrap methods are in close agreement. For larger 

samples, these two methods approximately provide the specified 95% coverage to the population mean, 

kθ, of a gamma distribution. For smaller sample sizes (from gamma distribution), the coverage provided 

by these two methods is slightly lower than the specified level of 0.95. For both lognormal and gamma 

distributions, these two methods (bootstrap t and Hall’s bootstrap) perform better than the other bootstrap 

methods, namely, the standard bootstrap method, simple percentile, and bootstrap BCA percentile 

methods. 

Just like the gamma distribution, for lognormally distributed data sets, it is noted that Hall’s UCL and 

bootstrap t UCL provide similar coverages. However, for highly skewed lognormal data sets, the 

coverages based upon Hall’s method and bootstrap t method are significantly lower than the specified 

coverage, 0.95 (Singh and Singh, 2003). This is true even in samples of larger sizes (e.g., n = 100). For 

lognormal data sets, the coverages provided by Hall’s bootstrap and bootstrap t methods do not increase 

much with the sample size, n. For highly skewed (e.g.,> 2.0) data sets of small sizes (e.g., n < 15), Hall’s 

bootstrap method (and also bootstrap t method) performs better than the Chebyshev UCL, and for larger 

samples, the Chebyshev UCL performs better than Hall’s bootstrap method. Similar to the bootstrap t 

method, it should be noted that Hall’s bootstrap method sometimes results in unstable, inflated, and 

erratic values, especially in the presence of outliers (Efron and Tibshirani, 1993). Therefore, these two 

methods should be used with caution. If outliers are present in a data set, then a 95% UCL of the mean 

should be computed using alternative UCL computation methods. 

2.5 Recommendations and Summary 

This section describes the recommendations and summary on the computation of a 95% UCL of the 

unknown population arithmetic mean, μ1, of a contaminant data distribution without censoring. These 

recommendations are based upon the findings of Singh, Singh, and Engelhardt (1997, 1999); Singh et al. 

(2002a); Singh, Singh, and Iaci (2002b); Singh and Singh (2003); and Singh et al. (2006). 

Recommendations have been summarized for: 1) normally distributed data sets, 2) gamma distributed 

data sets, 3) lognormally distributed data sets, and 4) data sets which are nonparametric and do not follow 

any of the three distributions included in ProUCL. 

58 
For skewed parametric as well as nonparametric data sets, there is no simple solution to compute a 95% 

UCL of the population mean, μ1. Singh et al. (2002a), Singh, Singh, and Iaci (2002b), and Singh and 

Singh (2003) noted that the UCLs based upon the skewness adjusted methods, such as the Johnson’s 

modified t and Chen’s adjusted-CLT, do not provide the specified coverage (e.g., 95%) to the population 

mean even for mildly to moderately skewed (e.g., σˆ in the interval [0.5, 1.0)) data sets for samples of 

sizes as large as 100. The coverage of the population mean by these skewness-adjusted UCL gets poorer 

(much smaller than the specified coverage of 0.95) for highly skewed data sets, where the skewness levels 

have been defined earlier as a function of σ or σˆ (standard deviation of log-transformed data). 

2.5.1 Recommendations to Compute a 95% UCL of the Unknown Population Mean, μ1, Using 

Symmetric and Positively Skewed Data Sets 

Interested users may want to consult graphs as given in Singh and Singh (2003) for a better understanding 

of the summary and recommendations made in this section. 

2.5.1.1 Normally or Approximately Normally Distributed Data Sets 

As expected, for a normal distribution, N (μ1, σ1

2

), the Student’s t-statistic, modified t-statistic, and 

bootstrap t 95% UCL computation methods result in UCLs which provide coverage probabilities close to 

the nominal level, 0.95. Contrary to the general conjecture, the bootstrap BCA method does not perform 

better than the other bootstrap methods (e.g., bootstrap t). Actually, for normally distributed data sets, the 

coverages for the population mean, μ1, provided by the UCLs based upon the BCA method and Hall’s 

bootstrap method are lower than the specified 95% coverage. This is especially true when the sample size, 

n is less than 30. For details refer to Singh and Singh (2003). 

• For normally distributed data sets, a UCL based upon the Student’s t-statistic, as given by 

equation (2-32), provides the optimal UCL of the population mean. Therefore, for 

normally distributed data sets, one should always use a 95% UCL based upon the 

Student’s t-statistic. 

• The 95% UCL of the mean given by equation (2-32) based upon Student’s t-statistic may 

also be used when the Sd, sy of the log-transformed data is less than 0.5, or when the data 

set approximately follows a normal distribution. A data set is approximately normal when 

the normal Q-Q plot displays a linear pattern (without outliers and jumps) and the 

resulting correlation coefficient is high (e.g., 0.95 or higher). 

• Student’s t-UCL may also be used when the data set is symmetric (but possibly not 

normally distributed). A measure of symmetry (or skewness) is k

ˆ

3 , which is given by 

equation (2-43). A value of k

ˆ

3 close to zero (e.g., if absolute value of skewness is 

roughly less than 0.2 or 0.3) suggests approximate symmetry. The approximate symmetry 

of a data distribution can also be judged by looking at histogram of data sets. 

2.5.1.2 Gamma Distributed Skewed Data Sets 

In practice, many skewed data sets can be modeled both by a lognormal distribution and a gamma 

distribution, especially when the sample size is smaller than 70-100. As is well known, the 95% H-UCL 

of the mean based upon a lognormal model often results in unjustifiably large and impractical 95% UCL 

59 
values. In such cases, a gamma model, G (k, θ), may be used to compute a reliable 95% UCL of the 

unknown population mean, μ1. 

• Many skewed data sets follow a lognormal as well as a gamma distribution. It should be 

noted that the population means based upon the two models could differ significantly. 

Lognormal model based upon a highly skewed (e.g., σˆ ≥ 2.5) data set will have an 

unjustifiably large and impractical population mean, μ1, and its associated UCL. The 

gamma distribution is better suited to model positively skewed environmental data sets. 

One should always first check if a given skewed data set follows a gamma distribution. If 

a data set does follow a gamma distribution or an approximate gamma distribution, one 

should compute a 95% UCL based upon a gamma distribution. Use of highly skewed 

(e.g., σˆ ≥ 2.5-3.0) lognormal distributions should be avoided. For such highly skewed 

lognormally distributed data sets that cannot be modeled by a gamma or an approximate 

gamma distribution, nonparametric UCL computation methods based upon the 

Chebyshev inequality may be used. 

• The five bootstrap methods do not perform better than the two gamma UCL computation 

methods. It is noted that the performances (in terms of coverage probabilities) of 

bootstrap t and Hall’s bootstrap methods are very similar. Out of the five bootstrap 

methods, bootstrap t and Hall’s bootstrap methods perform the best (with coverage 

probabilities for population mean closer to the nominal level of 0. 95). This is especially 

true when the skewness is quite high (e.g., k

ˆ < 0.1) and the sample size is small (e.g., n < 

10-15). This can be seen from graphs presented in Appendix A of the Technical Guide 

for ProUCL 3.0 (EPA, 2004). 

• The bootstrap BCA method does not perform better than the Hall’s method or the 

bootstrap t method. The coverage for the population mean, μ1, provided by the BCA 

method is much lower than the specified 95% coverage. This is especially true when the 

skewness is high (e.g., k

ˆ < 1) and sample size is small (Singh and Singh, 2003). 

• From the results presented in Singh, Singh, and Iaci (2002b), and in Singh and Singh 

(2003), it is concluded that for data sets which follow a gamma distribution, a 95% UCL 

of the mean should be computed using the adjusted gamma UCL when the shape 

parameter, k, is: 0.1 ≤ k < 0.5, and for values of k ≥ 0.5, a 95% UCL can be computed 

using an approximate gamma UCL of the mean, μ1. 

• For highly skewed gamma distributed data sets with k < 0.1, the bootstrap t UCL or 

Hall’s bootstrap (Singh and Singh, 2003) may be used when the sample size is smaller 

than 15, and the adjusted gamma UCL should be used when sample size starts 

approaching and exceeding 15. The small sample size requirement increases as skewness 

increases (that is as k decreases, the required sample size, n, increases). 

• The bootstrap t and Hall’s bootstrap methods should be used with caution as sometimes 

these methods yield erratic, unreasonably inflated, and unstable UCL values, especially in 

the presence of outliers. In the case Hall’s bootstrap and bootstrap t methods yield 

inflated and erratic UCL results, the 95% UCL of the mean should be computed based 

upon the adjusted gamma 95% UCL. ProUCL prints out a warning message associated 

60 
with the recommended use of the UCLs based upon the bootstrap t method or Hall’s 

bootstrap method. 

These recommendations for the use of gamma distribution are summarized in Table 2-3. 

Table 2-3. Summary Table for the Computation of a 95% UCL of the Unknown Mean, μ1, of a Gamma Distribution 

k

ˆ Sample Size, n Recommendation 

k

ˆ ≥ 0.5 For all n Approximate gamma 95% UCL 

0.1 ≤ k

ˆ < 0.5 For all n Adjusted gamma 95% UCL 

n < 15 95% UCL based upon bootstrap t k

ˆ < 0.1 or Hall’s bootstrap method* 

n ≥ 15 Adjusted gamma 95% UCL if available, k

ˆ < 0.1 otherwise use approximate gamma 95% UCL

*In case bootstrap t or Hall’s bootstrap methods yield erratic, inflated, and unstable UCL values, the UCL 

of the mean should be computed using adjusted gamma UCL. 

2.5.1.3 Lognormally Distributed Skewed Data Sets 

For lognormally, LN (μ, σ

2

), distributed data sets, the H-statistic-based UCL does provide specified 0.95, 

coverage for the population mean for all values of σ. However, the H-statistic often results in unjustifiably 

large UCL values that do not occur in practice. This is especially true when skewness is high (e.g., σ > 

2.0). The use of a lognormal model unjustifiably accommodates large and impractical values of the mean 

concentration and its UCLs. The problem associated with the use of a lognormal distribution is that the 

population mean, μ1, of a lognormal model becomes impractically large for larger values of σ, which in 

turn results in inflated H-UCL of the population mean, μ1. Since the population mean of a lognormal 

model becomes too large, none of the other methods except for the H-UCL provides the specified 95% 

coverage for that inflated population mean, μ1. This is especially true when the sample size is small and 

skewness is high. For extremely highly skewed data sets (with σ > 2.5-3.0) of smaller sizes (e.g., < 70­

100), the use of a lognormal distribution-based H-UCL should be avoided (e.g., see Singh et al., 2002a 

and Singh and Singh, 2003). Therefore, alternative UCL computation methods such as the use of a 

gamma distribution or use of a UCL based upon nonparametric bootstrap methods or Chebyshev 

inequality-based methods are desirable. 

As expected for skewed (e.g., with σ (or σˆ ) ≥ 0.5) lognormally distributed data sets, the Student’s tUCL, modified t-UCL, adjusted-CLT UCL, the standard bootstrap methods all fail to provide the 

specified 0.95 coverage for the unknown population mean for samples of all sizes. Just like the gamma 

distribution, the performances (in terms of coverage probabilities) of bootstrap t and Hall’s bootstrap 

methods are very similar (Singh and Singh, 2003). However, it is noted that the coverage provided by 

Hall’s bootstrap (and also by bootstrap t) is much lower than the specified 95% coverage for the 

population mean, μ1, for samples of all sizes of varying skewness. Moreover, the coverages provided by 

Hall’s bootstrap or bootstrap t method do not increase much with the sample size. 

61 
Also the coverage provided by the BCA method is much lower than the coverage provided by Hall’s 

method or the bootstrap t method. Thus, the BCA bootstrap method cannot be recommended to compute a 

95% UCL of the mean of a lognormal population. For highly skewed data sets of small sizes (e.g., < 15) 

with σ exceeding 2.5-3.0, even the Chebyshev inequality-based UCLs fail to provide the specified 0.95 

coverage for the population. However, as the sample size increases, the coverages provided by the 

Chebyshev inequality-based UCLs also increase. For such highly skewed data sets ( σˆ > 2.5) of sizes less 

than 10-15, Hall’s bootstrap or bootstrap t methods provide larger coverage than the coverage provided by 

the 99% Chebyshev (MVUE) UCL. Therefore, for highly skewed lognormally distributed data sets of 

small sizes, one may use Hall’s method (bootstrap t UCL) to compute an estimate of the EPC term. The 

small sample size requirement increases with σ. This means that as skewness (σ) increases, the sample 

size, n needed to provide specified coverage (e.g., ~0.95) by Hall’s bootstrap UCL also increases and 

becomes much larger than 20-30. 

It should be noted that even a small increase in the Sd, σ, increases the skewness considerably. For 

example, for a lognormal distribution, when σ = 2.5, skewness ≈ 11825.1; and when σ = 3, skewness ≈

729555. In practice, the occurrence of such highly skewed data sets (e.g., σ ≥ 3) is not very common. 

Nevertheless, these highly skewed data sets can arise occasionally and, therefore, require separate 

attention. Singh et al. (2002a) observed that when the Sd, σ, starts approaching 2.5 (that is, for lognormal 

data, when CV > 22.74 and skewness > 11825.1), even a 99% Chebyshev (MVUE) UCL fails to provide 

the desired 95% coverage for the population mean, μ1. This is especially true when the sample size, n, is 

smaller than 30. For such extremely skewed data sets, the larger of the two UCLs: the 99% Chebyshev 

(MVUE) UCL and the nonparametric 99% Chebyshev (Mean, Sd) UCL, may be used as an estimate of the 

EPC. 

It is also noted that, as the sample size increases, the H-UCL starts behaving in a stable manner. 

Therefore, depending upon the Sd, σ (actually its MLE σˆ ), for lognormally distributed data sets, one can 

use the H-UCL for samples of larger sizes such as greater than 70-100. This large sample size requirement 

increases as the Sd, σˆ , increases, as can be seen in Table 2-4. ProUCL can compute an H-UCL for 

samples of sizes up to 1000. For lognormally distributed data sets of smaller sizes, some alternative 

methods to compute a 95% UCL of the population mean, μ1, are summarized in Table 2-4. 

Furthermore, it is noted that for moderately skewed (e.g., σ > 1, 1.25) data sets of larger sizes (e.g., n > 

100-150), the H-UCL becomes even smaller than the sample mean or Student’s t-UCL (and various other 

UCLs). It should be pointed out that the large sample behavior of H-UCL has not been investigated 

rigorously. For confirmation purposes (that is H-UCL does provide the 95% coverage for larger samples 

also), it is desirable to conduct such a study for samples of larger sizes. 

Since skewness (as defined earlier) is a function of σ (or σˆ ), the recommendations for the computation of 

the UCL of the population mean are also summarized in Table 1-4 for various values of the MLE σˆ of σ

and the sample size, n. Here σˆ is an MLE of σ, and is given by the Sd of log-transformed data given by 

equation (2-2). Note that Table 2-4 is applicable to the computation of a 95% UCL of the population 

mean based upon lognormally distributed data sets without nondetect observations. A procedure to 

compute a 95% UCL of the mean of a lognormal distribution is summarized in the following steps: 

• Skewed data sets should be first tested for a gamma distribution. For lognormally 

distributed data sets (which can not be modeled by a gamma distribution), the method as 

summarized in Table 2-4 may be used to compute a 95% UCL of the mean. 

62 
Table 2-4. Summary Table for the Computation of a 95% UCL of the Unknown Mean, μ1, of a Lognormal Population 

σˆ Sample Size, n Recommendation 

σˆ < 0.5 For all n Student’s t, modified t, or H-UCL 

0.5 ≤ σˆ < 1.0 For all n H-UCL 

1.0 ≤ σˆ < 1.5 n < 25 95% Chebyshev (MVUE) UCL 

n ≥ 25 H-UCL 

n < 20 99% Chebyshev (MVUE) UCL 

1.5 ≤ σˆ < 2.0 20 ≤ n < 50 95% Chebyshev (MVUE) UCL 

n ≥ 50 H-UCL 

n < 20 99% Chebyshev (MVUE) UCL 

1.5 ≤ σˆ < 2.0 

20 ≤ n < 50 97.5% Chebyshev (MVUE) UCL 

50 ≤ n < 70 95% Chebyshev (MVUE) UCL 

n ≥ 70 H-UCL 

n < 30 Larger of (99% Chebyshev (MVUE) UCL or 

99% Chebyshev (Mean, Sd) 

2.5 ≤ σˆ < 3.0 30 ≤ n < 70 97.5% Chebyshev (MVUE) UCL 

70 ≤ n < 100 95% Chebyshev (MVUE) UCL 

n ≥ 100 H-UCL 

n < 15 Hall’s bootstrap method* 

3.0 ≤ σˆ ≤ 3.5 

15 ≤ n < 50 Larger of (99% Chebyshev (MVUE) UCL, 

99% Chebyshev(Mean, Sd) 

50 ≤ n < 100 97.5% Chebyshev (MVUE) UCL 

100 ≤ n < 150 95% Chebyshev (MVUE) UCL 

n ≥ 150 H-UCL 

σˆ > 3.5 For all n Use nonparametric methods* 

*In the case that Hall’s bootstrap method yields an erratic unrealistically large UCL value, then the UCL 

of the mean may be computed based upon the Chebyshev inequality. 

• Specifically, for highly skewed (e.g., 1.5 < σ ≤ 2.5) data sets of small sizes (e.g., n ≤ 50­

70), the EPC term may be estimated by using a 97.5% or 99% MVUE Chebyshev UCL of 

the population mean (or mass). For larger samples (e.g., n > 70), the H-UCL may be used 

to estimate the EPC. 

• For extremely highly skewed (e.g., σ > 2.5) lognormally distributed data sets, the 

population mean becomes unrealistically large. Therefore, the use of H-UCL should be 

avoided especially when the sample size is less than100. For such highly skewed data 

sets, Hall’s bootstrap UCL may be used when the sample size is less than 10-15 (Singh 

and Singh 2003). The small sample size requirement increases with σˆ . For example, n = 

10 is considered small when σˆ = 3.0, and n = 15 is considered small when σˆ = 3.5. 

• Hall’s bootstrap and bootstrap t UCL methods should be used with caution as sometimes 

it yields erratic, inflated, and unstable UCL values, especially in the presence of outliers. 

For these highly skewed data sets of size, n (e.g., less than 10-15), in the case that Hall’s 

63 
bootstrap method yields an erratic and inflated UCL value, the 99% Chebyshev MVUE 

UCL may be used to estimate the EPC term. ProUCL displays a warning message 

associated with the recommended use of Hall’s bootstrap method. 

2.5.1.4 Nonparametric Distribution-Free Skewed Data Sets without a Discernable Distribution 

• The use of gamma and lognormal distributions as discussed here will cover a wide range 

of skewed data distributions. For skewed data sets which are neither gamma nor 

lognormal, one can use a nonparametric Chebyshev UCL or Hall’s bootstrap UCL (for 

small samples) of the mean to estimate the EPC term. 

• For skewed nonparametric data sets with negative and zero values, use a 95% Chebyshev 

(Mean, Sd) UCL for the population mean, μ1. 

For all other nonparametric data sets with only positive values, the following procedure may be used to 

estimate the EPC term. 

• For mildly skewed data sets with σˆ ≤ 0.5, one can use Student’s t-statistic or modified tstatistic to compute a 95% UCL of mean, μ1. 

• For nonparametric moderately skewed data sets (e.g., σ or its estimate, σˆ in the interval 

(0.5, 1]), one may use a 95% Chebyshev (Mean, Sd) UCL of the population mean, μ1. 

• For nonparametric moderately to highly skewed data sets (e.g., σˆ in the interval (1.0, 

2.0]), one may use a 99% Chebyshev (Mean, Sd) UCL or 97.5% Chebyshev (Mean, Sd) 

UCL of the population mean, μ1, to obtain an estimate of the EPC term. 

• For highly skewed to extremely highly skewed data sets with σˆ in the interval (2.0, 3.0], 

one may use Hall’s UCL or the 99% Chebyshev (Mean, Sd) UCL to compute the EPC 

term. 

• Extremely skewed nonparametric data sets with σ exceeding 3.0 provide poor coverage. 

For such highly skewed data distributions, none of the methods considered provide the 

specified 95% coverage for the population mean, μ1. The coverages provided by the 

various methods decrease as σ increases. For such data sets of sizes less than 30, a 95% 

UCL can be computed based upon Hall’s bootstrap method or bootstrap t method. Hall’s 

bootstrap method provides highest coverage (but less than 0.95) when the sample size is 

small. It is noted that the coverage for the population mean provided by Hall’s method 

(and bootstrap t method) does not increase much as the sample size, n, increases. 

However, as the sample size increases, coverage provided by the 99% Chebyshev (Mean, 

Sd) UCL method also increases. Therefore, for larger samples, a UCL should be 

computed based upon the 99% Chebyshev (Mean, Sd) method. This large sample size 

requirement increases as σˆ increases. These recommendations are summarized in Table 

2-5. 

64 
Table 2-5. Summary Table for the Computation of a 95% UCL of the Unknown Mean, μ1, Based Upon a Skewed Data Set 

(with all Positive Values) without a Discernable Distribution, Where σˆ is the sd of Log-transformed Data 

σˆ Sample Size, n Recommendation 

σˆ ≤ 0.5 For all n 95% UCL based on Student’s t- or Modified-t statistic 

0.5 < σˆ ≤ 1.0 For all n 95% Chebyshev (Mean, Sd) UCL 

1.0 < σˆ ≤ 2.0 

n < 50 99% Chebyshev (Mean, Sd) UCL 

n ≥ 50 97.5% Chebyshev (Mean, Sd) UCL 

2.0 < σˆ ≤ 3.0 

n < 10 Hall’s Bootstrap UCL* 

n ≥ 10 99% Chebyshev (Mean, Sd) UCL 

3.0 < σˆ ≤ 3.5 

n < 30 Hall’s Bootstrap UCL* 

n ≥ 30 99% Chebyshev (Mean, Sd) UCL 

σˆ > 3.5 

n < 100 Hall’s Bootstrap UCL* 

n ≥ 100 99% Chebyshev (Mean, Sd) UCL 

*If Hall’s bootstrap method yields an erratic and unstable UCL value (e.g., happens when outliers are 

present), a UCL of the population mean may be computed based upon the 99% Chebyshev (Mean, Sd) 

method. 

2.5.2 Summary of the Procedure to Compute a 95% UCL of the Unknown Population Mean, 

μ1, Based Upon Full Data Sets without Nondetect Observations 

1. The first step in computing a 95% UCL of a population arithmetic mean, μ1, is to perform 

goodness-of-fit tests to test for normality, lognormality, or gamma distribution of the data 

set under study. ProUCL has three methods to test for normality or lognormality: the 

informal graphical test based upon a Q-Q plot, the Lilliefors test, and the Shapiro-Wilk W 

test. ProUCL also has three methods to test for a gamma distribution: the informal 

graphical Q-Q plot based upon gamma quantiles, the Kolmogorov-Smirnov (K-S) EDF 

test, and the Anderson-Darling (A-D) EDF test. 

ProUCL generates a quantile-quantile (Q-Q) plot to graphically test the normality, 

lognormality, or gamma distribution of the data. There is no substitute for graphical 

displays of a data set. On this graph, a linear pattern (e.g., with high correlation such as 

0.95 or higher) displayed by bulk of data suggests approximate normality, lognormality, 

or gamma distribution. On this graph, points well separated from the majority of data 

may be potential outliers requiring special attention. Also, any visible jumps and breaks 

of significant magnitudes on a Q-Q plot suggest that more than one population may be 

present. In that case, each of the populations should be considered separately. That is, a 

separate EPC term should be computed for each of the populations. 

2. It is, therefore, recommended to always use the graphical Q-Q plot as it provides useful 

information about the presence of multiple populations (e.g., site and background data 

mixed together) or outliers. Both graphical Q-Q plot and formal goodness-of-fit tests 

65 
should be used on the same data set before determining the distribution of the data set 

under investigation. A single test statistic such as the Shapiro-Wilk test (A-D test or some 

other GOF test) may lead to the incorrect conclusion that the data are normally (or 

gamma) distributed even when there are more than one population present. Only a 

graphical display, such as an appropriate Q-Q, can provide this kind of important 

information. Obviously, when multiple populations are present, those should be separated 

out and the EPC terms (the UCLs) or other estimates (e.g., BTVs) should be computed 

separately for each of those populations. Therefore, it is strongly recommended not to 

skip the GOF tests option in ProUCL 4.0. Since the computation of an appropriate UCL 

depends upon data distribution, it is advisable that the user should take his time (instead 

of blindly using a numerical value of a test statistic in an effort to automate the 

distribution selection process) to determine the data distribution. Both graphical (e.g., QQ plots) and analytical procedures (Shapiro-Wilk test, K-S test) should be used on the 

same data set to determine the most appropriate distribution of the data set under study. 

3. After performing the goodness-of-fit test, ProUCL informs the user about the data 

distribution: normal, lognormal, gamma distribution, or a non-discernable distribution. 

4. For a normally distributed (or approximately normally distributed) data set, the user is 

advised to use Student’s t-distribution-based UCL of the mean. Student’s t-distribution 

(or modified t-statistic) may also be used to compute the EPC term when the data set is 

k

ˆ symmetric (e.g., is smaller than 0.2-0.3) or mildly skewed; that is, when σ or σˆ is 3 

less than 0.5. 

5. For gamma distributed (or approximately gamma distributed) data sets, the user is 

advised to: use the approximate gamma UCL for k

ˆ ≥ 0.5; use the adjusted gamma UCL 

for 0.1 ≤ k

ˆ < 0.5; use the bootstrap t method (or Hall’s method) when kˆ < 0.1 and the 

sample size, n < 15; and use the adjusted gamma UCL (if available) for k

ˆ < 0.1 and 

sample size, n ≥ 15. If the adjusted gamma UCL is not available, then use the 

approximate gamma UCL as an estimate of the EPC term. In the case that the bootstrap t 

method or Hall’s bootstrap method yields an erratic inflated UCL (e.g., when outliers are 

present) result, the UCL should be computed using the adjusted gamma UCL (if 

available) or the approximate gamma UCL. 

6. For lognormal data sets, ProUCL recommends (as summarized in Table 2-4) a method to 

estimate the EPC term based upon the sample size and standard deviation of the logtransformed data, σˆ . ProUCL can compute an H-UCL of the mean for samples of sizes 

up to 1000. Nonparametric UCL computation methods such as the modified t, CLT 

method, adjusted-CLT method, bootstrap and jackknife methods are also included in 

ProUCL. However, it is noted that nonparametric UCLs based upon most of these 

methods do not provide adequate coverage to the population mean for moderately skewed 

to highly skewed data sets (e.g., Singh and Singh, 2003). 

66 
7. For data sets, which are not normally, lognormally, or gamma distributed, a 

nonparametric UCL of the mean based upon the Chebyshev inequality is preferred. The 

Chebyshev (Mean, Sd) UCL does not depend upon any distributional assumptions and 

can be used for moderately to highly skewed data sets which do not follow any of the 

three data distributions incorporated in ProUCL. 

8. It should be noted that for extremely skewed data sets (e.g., with σˆ exceeding 3.0), even 

a Chebyshev inequality-based 99% UCL of the mean fails to provide the desired 

coverage (e.g., 0.95) of the population mean. A method to compute the EPC term for 

distribution-free data sets is summarized in Table 2-5. It should be pointed out that in the 

case that Hall’s bootstrap method appears to yield erratic and inflated results (typically 

happens when outliers are present), the 99% Chebyshev UCL may be used as an estimate 

of the EPC term. 

67 
68 
Chapter 3 

Estimating Background Threshold Values or Establishing SiteSpecific Background Concentrations Using Full Data Sets 

without Nondetect (ND) Observations 

3.1 Introduction 

Often in environmental applications, site-specific background level contaminant concentrations are 

needed to compare site concentrations (e.g., both before and after some remediation activities) with 

background level contaminant concentrations, also called as background statistics or background 

threshold values (BTVs). These BTVs are computed based upon the sampled data collected from the sitespecific background as determined by all interested parties, including the potentially responsible parties, 

local, and federal government agencies. Many times, intermediate or future remediation decisions at a 

polluted site are made after performing such background versus site comparisons. A site observation 

exceeding a BTV can be viewed as coming from a contaminated area of the site under study. It is, 

therefore, important that these background statistics be computed using appropriate background data sets 

and defensible statistical methods. Some minimum sample size requirements (e.g., sample size >8-10) to 

estimate the BTVs based upon background data sets have been discussed in Chapter 1 of this guidance 

document. Chapter 1 also discusses situations when it may be appropriate to perform point-by-point site 

observations (preferably composite samples) comparisons with BTVs or with some pre-established 

threshold values. Specifically when not more than 4-6 site observations need to be compared individually 

with estimated or pre-established BTVs, one may compare point-by-point site observations with BTVs 

and other threshold values. If more than 8-10 (preferably more) site observations are available, then it is 

preferable to use single sample hypothesis (in case BTVs are pre-established) or two-sample hypothesis 

(in case BTVs need to be estimated using background data) testing approaches to perform site versus 

background comparisons. This chapter describes statistical limits that may be used to estimate the BTVs 

and other not-to-exceed values for full data sets without any nondetect (ND) observations. Statistical 

limits based upon data sets with nondetect observations are discussed in Chapter 5. Chapter 6 discusses 

the various single sample and two-sample hypotheses testing approaches for data sets with and without 

NDs as incorporated in ProUCL 4.0. 

It should be pointed out that the availability of background statistics as discussed in this chapter is 

particularly useful when individual site observations from impacted areas of the site (perhaps after some 

remediation activities) are compared with some BTVs to determine if enough remediation (at the 

impacted areas of the site) has been performed yielding remediated site concentrations which are 

comparable to background level concentrations. This method of site versus background comparisons is 

also useful when not enough site data are available to perform two-sample comparisons such as the t-test 

or the nonparametric Wilcoxon Rank Sum (WRS) test. Moreover, in practice, during remediation 

activities, it is desirable to compare each individual site observation (collected during remediation phase) 

with some pre-determined or estimated background level threshold value(s). Sometimes pre-established 

screening levels are used as estimates of background threshold values. However, in practice, these BTVs 

need to be estimated based upon site-specific background (or reference) data sets collected using 

appropriate sampling methods and data quality objectives (DQOs). This chapter describes procedures, 

which can be used to compute relevant background statistics based upon an appropriate background data 

set without any nondetect observations. Methods to estimate the BTVs based upon data sets with NDs are 

described in Chapter 5 of this Technical manual. 

69 
When enough site and background data are available, it is recommended to use two-sample tests (t-test, 

WRS test, etc.) to perform background versus site comparisons. Parametric and nonparametric procedures 

(hypotheses testing) can be used to compare the measures of central tendencies of the two populations 

(background versus site) when enough detected data are available from the two populations under 

consideration. Hypothesis testing approaches to perform site versus background comparisons are 

discussed in Chapter 6 of this Technical guidance document. 

This chapter (and also Chapter 5) deals with the computation of background statistics (BTVs) when it is 

known/assumed that the underlying data set does represent a sample collected from some site-specific 

background area(s). That is, it is assumed a priori that all of the observations (at least most of them) come 

from a single background population. However, since outliers are inevitable in most environmental 

applications, some outliers may also be present in a background data set. These outlying observations 

need to be identified before computing the background statistics as outliers, when present, distort all of 

the statistics of interest (such as background statistics), which in turn may lead to incorrect remediation 

decisions for the site under study. The inclusion (or exclusion) of outliers in a background data set needs 

to be justified before performing other relevant statistical analyses. All interested parties should be 

involved in such decision making to determine the inclusion or exclusion of outliers in a background data 

set. The proper identification of multiple outliers is a complex issue and is beyond the scope of this 

document. A brief description of outlier identification is given in Section 1. A couple of outlier tests as 

incorporated in ProUCL 4.0 are given in Chapter 7 of this Technical document. Some discussions about 

the disposition of outliers are provided in Chapter 3 of the revised Guidance for Comparing Background 

and Chemical Concentrations in Soil for CERCLA Sites (EPA, 2002b). 

A more complicated problem arises when the data set under study represents a mixture sample, which is 

inevitable in many environmental applications. In these cases, the data set under study may consist of 

samples from the background areas as well as from various areas of the site under study. In this situation, 

first one has to separate the background observations from other site related observations. After the 

background data set has been properly identified, one can proceed with the computation of background 

statistics as presented later in this chapter. However, separation of background data from a mixture 

sample is not an easy task. Using the population partitioning techniques, statisticians (e.g., see Singh, 

Singh, and Flatman, 1994) have developed some background separation methods from mixture samples. 

However, the topics of population partitioning and the identification of a valid background data set from 

the mixture sample are beyond the scope of ProUCL 4.0 and this guidance document. It requires 

developing a separate chapter, which will deal with the population partitioning methods including the 

identification of a valid background data set from a mixture sample. Throughout this chapter, it is 

assumed that one is dealing with a sample from a single population representing a valid site-related 

background data set. 

The first step in establishing site-specific background level contaminant concentrations for site-related 

hazardous analytes is to perform background sampling to collect an appropriate number of samples from 

the designated site-specific background areas, perhaps using the input from experts and personnel familiar 

with the site operations and history. An appropriate DQO process should be followed to collect an 

adequate number of background samples. Once the adequate amount of data has been collected, the next 

step is to determine the data distribution. This is typically done using exploratory graphical tools as well 

as formal goodness-of-fit tests. These tests are described in several environmental documents (EPA 2006, 

ProUCL 2004, and Navy documents 1999, 2002a, 2002b). In addition to graphical displays, ProUCL 4.0 

has goodness-of-fit (GOF) tests for normal, lognormal, and gamma distributions. 

70 
Once the data distribution of a background data set has been determined, one can use parametric or 

nonparametric statistical methods to compute background statistics. Several upper limits have been 

incorporated in ProUCL 4.0 that may be used as estimated of BTVs and other not-to-exceed values. A 

review of the environmental literature reveals that one or more of the following statistical limits are used 

to compute the background statistics; that is, to determine background level contaminant concentrations, 

BTVs. The BTVs are called upper background cutoff levels. 

1. Upper percentiles 

2. Upper prediction limits (UPLs) 

3. Upper tolerance limits (UTLs) 

4. IQR Upper Limit (upper end of the upper whisker in a box and whisker plot) 

Depending upon the background data distribution, the background upper limits listed above can be 

computed by using parametric methods based upon probability distributions (e.g., normal, lognormal, or 

gamma) or by using nonparametric (distribution-free) methods. The background limits listed above are 

often used as background threshold values to compare individual site observations with background level 

contamination. Typically, a site observation (possibly based upon composite samples) in exceedance of a 

background threshold value can be considered as coming from a contaminated site area that may have 

been impacted by the site-related activities. In other words, such a site observation may be considered as 

exhibiting some evidence of contamination at the site due to site-related activities. In case of an 

exceedance of the BTV by a site location, some practitioners like to verify the possibility of contaminated 

site location by re-sampling that location, and comparing the sample value with the BTV. 

The background threshold values are used when not enough site data (e.g., < 4-6 observations) are 

available to perform traditional two-sample comparisons (e.g., t-test, Wilcoxon Rank Sum test, Gehan’s 

test, etc.) as described in Chapter 6 of this document. In the absence of adequate amount of site data, 

individual point-by-point site observations have to be compared with some BTVs to determine the 

presence or absence of contamination due to site related activities. This method of comparing site versus 

background level contamination is particularly helpful to use after some sort of remediation activities 

have taken place at the site; and the objective is to determine if the remediated site areas have been 

remediated enough to the background level contaminant concentrations. A brief discussion of 

identification and disposition of outliers is considered first. 

3.2 Treatment of Outliers 

While computing reliable background statistics, it is essential that one is dealing with a single population 

representing site background without potentially impacted observations (outliers). Therefore, a brief 

discussion on this topic is presented in this section. As well known, outliers, when present, typically 

represent observations from different populations(s), perhaps contaminated observations from the site 

under study. Outliers distort all of the statistics of interest, including the sample mean, the sample 

standard deviation, and, consequently, the parametric percentiles, and various upper limits such as UPLs, 

UTLs, and UCLs. It is noted that nonparametric upper percentiles are often represented by higher ordered 

statistics such as the largest value or the second largest value. In the case of extreme high observations, 

these higher order statistics may be outlying observations representing contaminated observations from 

the site (e.g., a large Federal Facility) under study. Decisions made based upon outliers or distorted 

71 
statistics can be incorrect and misleading. Therefore, special attention should be given to such outlying 

observations. 

If justified, that is, if some outliers do represent observations from the contaminated areas of the site, then 

those observations should not be included in the computation of BTVs. This decision should be a team 

effort to determine whether or not an identified outlier does represent an observation from the 

contaminated part of the site. Such an outlying observation should not be part of the background data set. 

Specifically, such an observation should not be used in the computation of background statistics. All 

interested parties should be involved in making such decisions. Several classical (EPA, 2006) and robust 

(Singh and Nocerino, 1995) statistical procedures are available to identify multiple outliers. Robust and 

resistant outlier identification procedures are beyond the scope of ProUCL 4.0. In environmental 

applications (EPA, 2006 and Navy, 2002a, 2002b), classical procedures are used to identify outliers. A 

couple of those classical outlier tests are available in ProUCL 4.0. As mentioned before, classical outlier 

procedures suffer from masking effects as they get distorted by the same outlying observations that they 

are supposed to find! It is suggested to use robust and resistant statistical procedures to identify multiple 

outliers. Several robust outlier identification procedures are available in Scout (EPA, 1999) software 

package, which is currently under revision and upgrade. It is recommended to supplement the use of 

classical and robust procedures with graphical procedures such as box plots, quantile-quantile (Q-Q) 

plots. 

Note: It should be noted that the methods as incorporated in ProUCL 4.0 can be used on any data set 

(with or without nondetects) with or without the potential outliers. Specifically, it should not be 

misunderstood that ProUCL 4.0 is restricted to be used only on data sets without outliers. It is not a 

requirement to delete or omit the outliers before using estimation, UCL95, and various other limits 

computation methods (e.g., KM (BCA) UCL, MLE) as incorporated in ProUCL 4.0. The fact of the matter 

is that the user should be aware of the fact that the inclusion of a few outliers in the computations of these 

statistics may yield distorted estimates, UCL95, UPLs, UTLs, and various other statistics. Therefore, for 

more accurate and reliable statistics and results, the authors of this Technical Guide recommend that 

whenever justified, the low probability outlying observations (often coming from different population(s)) 

should not be included in the computation of the statistics used in the various decision making processes. 

The statistics (e.g., upper limits) of interest should be computed using the majority of the data set 

representing the dominant population (e.g., an AOC, a background area). The outlying observations 

should be separately investigated to determine the reasons for their occurrences (e.g., errors or 

contaminated locations). It is always a good practice to compute the statistics with and without the 

outliers, and compare the potential impact of outliers on the decision making processes. 

Throughout this chapter, x1, x2, ... , xn represent the background concentrations for a contaminant of 

potential concern (COPC) collected from some site-specific background or reference area. The objective 

is to estimate a BTV based upon this data set. The sample values are arranged in ascending order. The 

resulting ordered sample (called ordered statistics) is denoted by x(1) ≤ x(2) ≤ ... ≤ x(n). The ordered 

statistics are often used as nonparametric estimates of upper percentiles, upper prediction limits (UPLs), 

and upper tolerance limits (UTLs). Also, let yi = ln (xi); i = 1, 2, ... , n, then y and sy represent the mean 

and standard deviation (sd) of the log-transformed data. Some parametric and nonparametric upper limits 

often used to estimate BTVs are described throughout the rest of this chapter. 

72 
3.3 Upper p*100% Percentiles as Estimates of Background Threshold 

Values (BTVs) 

Some Navy documents (1999, 2002a, 2002b) suggest the use of the 95th upper percentiles of the 

background distribution as estimates of the background level threshold values (e.g., pages 46, 137 Navy 

2002b). However, explicit parametric formulae for the computation of the 95th percentiles are missing 

from the Navy (e.g., 2002a, 2002b) documents, making it difficult for a typical user to use the 95% 

percentiles as estimates of BTVs. Since these percentiles do represent a feasible method to compute 

background threshold values, (one of the objectives of the present document), for clarification, 

computation of both parametric as well as nonparametric percentiles are briefly described as follows. 

In most statistical textbooks (e.g., Hogg and Craig, 1995), the pth (e.g., p = 0.95) sample percentile (of the 

measured sample values) is defined as that value, xˆ 

p , such that p100% of the background data set lies at 

or below it. The carat sign over xp, indicates that it represents a statistic (an estimate of the p

th population 

percentile) computed based upon the sampled data. 

3.3.1 Nonparametric p*100% Percentile 

It is quite simple to compute a nonparametric 95% percentile of a background data set. It should be 

pointed out that such nonparametric sample percentiles (for 0 < p <1) cannot exceed the maximum value 

in a background data set. These nonparametric 95% percentiles may be used when the background data 

(raw or transformed) do not follow a normal or a gamma distribution at some specified (e.g., d = 0.05, 

0.1) level of significance. 

It is noted that, the practitioners compute these nonparametric p

th percentiles (quantiles) in more than one 

way. Some users compute the p

th using the pnth order statistic, which may be a whole number between 1 

and n or a fraction lying between 1 and n. For example, if n = 20, and p = 0.95, then 20*0.95 = 19, thus 

the 19th ordered statistic represents the 95% percentile. If n = 17, and p = 0.95, then 17*0.95= 16.15, thus 

the 16.15th ordered value may be used as an estimate of the BTV. The 16.15th ordered value lies between 

the 16th and the 17th order statistics and can be computed by using simple linear interpolation given by: 

x(16.15) = x(16) + 0.15 (x(17) - x(16) ). (3-1) 

It is noted that some other users compute the p

th nonparametric percentile by the order statistic given by 

the (pn+0.5) th order statistic, while others compute the p

th nonparametric percentile by the order statistic 

given by the (p*(n+1)) th order statistic. As mentioned above, if the number computed in not a whole 

number, then the percentile is computed using the linear interpolation illustrated above. In any case, if for 

a given value of p, the resulting number, (p*(n+1)) exceeds n, then that p

th percentile is estimated by the 

n

th order statistic, that is by the maximum value. In ProUCL 4.0, the pth nonparametric percentile is 

estimated by the (p*(n+1)) th order statistic. This formula is used on data sets with and without ND 

observations. 

3.3.2 Normal p*100% Percentile 

The computation of normal upper percentiles has been considered next. First, compute the sample mean, 

x , and standard deviation (sd), s, using a defensible (e.g., outliers, multiple populations, mixture 

populations are not allowed) background data set without the outliers. For normally distributed data sets, 

the p*100th sample percentile is given by the following statement. 

73 
xˆ = x + sz (3-2) p p 

Here zp is the p*100th percentile of a standard normal, N (0,1), distribution, which means that the area 

(under the standard normal curve) to the left of zp is p. If the distributions of the site data and the 

background data are comparable and similar (meaning no contamination due to the site related activities), 

then an observation coming from a population (e.g., site) similar (comparable) to that of the background 

population should lie at or below the p*100% upper percentile, xˆ 

p , with probability p. Thus, the 95% 

percentile given by the above equation (for p = 0.95 or 0.99) may also be used as an estimate of the 

background threshold value when the background data are normally distributed. 

3.3.3 Lognormal p*100% Percentile 

To compute the p

th upper percentile, xˆ 

p , of a lognormally distributed data set, the sample mean, y , and 

standard deviation (sd), sy, of log-transformed data are computed first using a defensible background data 

set without outliers. For lognormally distributed data sets, the p*100th percentile is given by the following 

statement, 

xˆ 

p = exp(y + s y z p ) , (3-3) 

where, as before, zp is the upper p*100th percentile of a standard normal, N(0,1), distribution. A 95th 

percentile given by the above equation may be used as an estimate of the BTV for a COPC when the 

background data are lognormally distributed. 

3.3.4 Gamma p*100% Percentile 

Since the introduction of a gamma distribution, G (k, θ), is relatively new in environmental applications 

(e.g., Singh, Singh, and Iaci 2002), a brief description of the gamma distribution is given first. The 

equations giving the maximum likelihood estimates (MLEs) of the gamma parameters, k (= shape 

parameter) and θ (= scale parameter), can be found in Singh, Singh, and Iaci (2002) and also in the 

ProUCL 3.0 Technical Guide (EPA, 2004). A random variable (RV), X (e.g., Aroclor 1254 

concentrations), follows a gamma distribution, G (k,θ), with parameters k > 0 and θ > 0, if its probability 

density function is given by the following equation: 

f (x; k,θ) = θ k Γ

1

(k) 

x

k −1

e −x θ; x > 0 

(3-4) 

= 0; otherwise 

The mean, variance, and skewness of a gamma distribution are given by: mean = μ = kθ, variance = σ

2

 = 

kθ

2

, and skewness = 2 / k . Note that as k increases, the skewness decreases, and, consequently, a 

gamma distribution starts approaching a normal distribution for larger values of k (e.g., k ≥ 6 - 8). 

Let k

ˆ and θ

ˆ represent the maximum likelihood estimates (MLEs) of k and θ respectively. Note the 

relationship between a chi-square and a gamma distribution. Specifically, the relationship between a 

gamma RV, X = G (k, θ), and a chi-square RV, Y, is given by X = Y * θ /2, where Y follows a chi-square 

distribution with 2k degrees of freedom. Thus, the percentiles of a chi-square distribution (as programmed 

74 
in ProUCL) can be used to determine the percentiles of a gamma distribution. In practice, k is replaced by 

its MLE. Thus, once an α*100% percentile, y(α) , of a chi-square distribution with 2k degrees of freedom is 

obtained, the α*100% percentile for a gamma distribution can be obtained by using the equation: 

xα = yα *θ /2 (3-5) 

3.3.5 Example 1 

Consider a site-specific background data set associated with a Superfund site. The data set has several 

inorganic contaminants of potential concern, including aluminum, arsenic, chromium, and lead. The 

computation of background statistics obtained using ProUCL 4.0 are summarized in this example. The 

complete data set is given in Appendix 5 of the Guidance for Comparing Background and Chemical 

Concentrations in Soil for CERCLA Sites (EPA, 2002b). 

3.3.5.1 Normal Percentile 

Using the Shapiro-Wilk test as incorporated in ProUCL, it is determined that aluminum follows a normal 

distribution at 5% significance level. This can also be seen from the Q-Q plot as given in Figure 3-1. 

Since the data set follows a normal distribution, a normal 95% upper percentile may be used as an 

estimate of the BTV. The sample mean of aluminum data set is 7789.1667, the standard deviation, s, is 

4263.8969, and z0.95, the upper 95% percentile of a standard normal distribution, is 1.6449. Thus normal 

95% percentile for aluminum is: 

xˆ0.95 = x + sz0.95 = 7789.1667 + 4263.8969 * 1.6449 = 14802.8507 

Figure 3-1. Shapiro-Wilk Normal Distribution Test for Aluminum 

75 
3.3.5.2 Lognormal Percentile 

Using ProUCL 4.0, it is determined that chromium concentrations follow a lognormal distribution at 5% 

level of significance. This can also be seen from the chromium Q-Q as given in Figure 3-2. The sample 

mean and standard deviation of the log-transformed data are y = 2.3344, and sy = 0.5678. Thus the 95% 

upper percentile for chromium is given by the following equation: 

xˆ0.95 = exp( y + s y z0.95 ) = exp(2.3344 + 0.5678 * 1.6449) = 26.26868 

Figure 3-2. Shapiro-Wilk Lognormal Distribution Test for Chromium 

3.3.5.3 Nonparametric Percentile 

Using ProUCL, it is determined that Lead (Pb) concentrations do not follow any of the known 

distributions as incorporated in ProUCL 4.0. Therefore, an upper nonparametric 95th percentile may be 

used as an estimate of the BTV for lead concentrations. A 95% nonparametric upper percentile is given 

by the 0.95*n

th order statistic for n=24, 

95% Upper Percentile = x(0.95n) = x(22.8) = x(22) + 0.8(x(23) - x(22)). 

The value for x(22) is 53.3 and for x(23) is 98.5. Thus, a 95% Upper Percentile = 53.3 + 0.8 * (98.5 – 53.3) 

= 89.46. 

3.3.5.4 Gamma Percentile 

Using ProUCL, it is determined that Arsenic (As) concentrations follow a gamma distribution. The 

gamma Q-Q plot displaying Anderson-Darling test statistic is given in Figure 3-3. 

76 
The bias-corrected MLE for k is 3.6616 and the bias-corrected MLE of θ is 0.5867. The 95% percentile 

for a chi-square distribution with degrees of freedom (df) 2k

ˆ is yα = 14.5362. Using these values, one can 

derive the 95% gamma percentile as follows: 

xα = yα = θ / 2 = (14.5362 * 0.5867) / 2 = 4.2643. 

It is noted that arsenic concentrations also follow a lognormal distribution. Therefore, for comparison, 

several upper percentiles are tabulated as follows in Table 3-1. This also includes the normal percentile 

even though arsenic does not follow a normal distribution. 

Figure 3-3. Anderson-Darling Gamma Distribution Test 

Table 3-1. 95% Percentiles for Arsenic 

Distribution 95% Percentile 

Normal 4.0564 

Gamma 4.2643 

Lognormal 4.3877 

Non-discernable 3.7000 

The 1989 RCRA document is one of the early EPA guidance documents. The RCRA document talks 

about the use of both 95% UTLs and 95% UPLs to determine the presence or absence of contamination in 

compliance wells. The UPLs or UTLs are computed based upon the background data sets (e.g., upstream 

data, upgradient wells, site-specific background). Compliance well observations (or site data) are then 

compared with the background well (or background data) UTL or UPL. An exceedance in compliance 

well may suggest some evidence of contamination in that well. Similar arguments can be made when 

comparing concentrations of soil samples from an impacted site area with some site-specific background 

concentrations. 

77 
In 1992, EPA upgraded the 1989 RCRA document and came up with its addendum. This 1992 addendum 

also talks about the use of UTLs and UPLs as estimates of background level threshold values. The 1992 

addendum modifies the formula for 95% UTL as given in the 1989 RCRA document (page 51, Chapter 

4). The multipliers, k (used to compute a 95% UTL), as given in the 1989 document, are meant to provide 

at least 95% coverage. The 1992 addendum states that, in practice, the use of this factor, k, as given in 

Table 2-5 of the 1989 document, provides 98% (and not 95%) coverage. This is especially true when one 

is dealing with samples of small sizes. Thus, the use of factor k (to compute a UTL), as described above, 

may result in more false negatives (larger background statistics), which is not protective of the 

environment and human health. Therefore, a modified multiplier, which is the same as the prediction limit 

multiplier, has been suggested in the 1992 RCRA addendum. In the 1992 addendum, it is stated that this 

modified multiplier will on the average provide the specified coverage (= 95% here). 

However, it desirable that these statements about the coverage’s provided by the 95% UTLs and 95% 

UPLs be verified by Monte Carlo simulation experiments. As mentioned before, BTVs are often used to 

make remediation decisions at polluted sites. Therefore, these BTVs should be computed using defensible 

statistical procedures. This section describes the computation of UTLs as well as UPLs. Both parametric 

as well as nonparametric UTL and UPL computation procedures have been summarized in the following 

sections. 

3.4 Upper Tolerance Limits 

For many environmental applications, such as the groundwater monitoring applications, an upper 

tolerance limit (UTL) is often used to provide appropriate coverage for a proportion, p% (e.g., 80%, 90%, 

95%, coverage, etc.) of future observations with confidence coefficient, (1 – α). It should be noted that an 

upper tolerance limit with confidence coefficient, (1 – α), covering a proportion of p% (p is also called the 

coverage coefficient), observations simply represent a (1 – α)100% upper confidence limit of the p

th 

percentile of the population under study (here the background population). 

3.4.1 Normal Upper Tolerance Limits 

First, compute the sample mean, x , and standard deviation (sd), s, using a defensible (e.g., outliers, 

multiple populations, mixture populations not allowed) background data set without the outliers (e.g., see 

Example 2 below). For normally distributed data sets, an upper (1 – α)100% tolerance limit with tolerance 

or coverage coefficient = p (that is providing coverage to at least p100% proportion of observations) is 

given by the following statement. 

UTL = x + K * s (3-6) 

Here, K = K (n, α, p) is the tolerance factor and depends upon the sample size, n, confidence coefficient = 

(1 – α), and the coverage proportion = p. The UTL given by the above equation represents a (1 – α)100% 

confidence interval for the p

th percentile of the underlying normal distributions. The values of the 

tolerance factor, K, have been tabulated extensively in the various statistical books (e.g., Hahn and 

Meeker 1991). Those K values are based upon non-central t-distributions. Also, some large sample 

approximations (e.g., Natrella, 1963) are available to compute the K values for one-sided tolerance 

intervals (same for both UTLs and lower tolerance limit). The approximate value of K is also a function 

of the sample size, n, coverage coefficient, p, and the confidence coefficient, (1 – α). In the ProUCL 4.0 

software package, the values of K for samples of sizes ≤ 30, as given in Hahn and Meeker, have been 

directly programmed. For sample sizes larger than 30, the large sample approximations, as given in 

Natrella (1963), have been used to compute the K values. The Natrella’s approximation seems to work 

78 
well for samples of sizes larger than 30 (Hahn and Meeker 1991). The program, ProUCL, seems to work 

well to compute these K values for sample of sizes as large as 5000 (and even larger). 

3.4.2 Lognormal Upper Tolerance Limits 

The procedure to compute UTLs for lognormally distributed data sets is similar to that for normally 

distributed data sets. First, the sample mean, y , and standard deviation (sd), sy, of the log-transformed 

data are computed using a defensible unique background data set without outliers. An upper (1 – α)100% 

tolerance limit with tolerance or coverage coefficient = p (that is, providing coverage to at least p100% 

proportion of observations) is given by the following statement. 

 UTL = exp( y + K * s y ) (3-7) 

Note that, just as for the normal distribution, the UTL given by the above equation represents a 

(1 – α)100% confidence interval for the p

th percentile of the lognormal distribution. The K factor used to 

compute the lognormal UTL is the same as the one used to compute the normal UTL. It should be noted 

that just as the upper confidence limits (UCLs) for the mean of lognormally distributed populations, the 

UTLs based upon lognormal distributions are typically higher (sometimes unrealistically higher as shown 

in the following example) than other parametric and nonparametric UTLs. The use of a lognormal UTL to 

estimate the BTV should be specifically avoided when skewness is high (e.g., sd of logged data > 1, 1.5) 

and sample size is small (e.g., < 30, 50). 

3.4.3 Gamma Distribution Upper Tolerance Limits 

Positively skewed environmental data sets can often be modeled by a gamma distribution. Also, ProUCL 

software has two goodness-of-fit tests (Anderson-Darling test and Kolmogorov-Smirnov test) to test for 

gamma distributions. Bootstrap methods: percentile (%) bootstrap and bias-corrected accelerated (BCA) 

bootstrap methods can be used to compute UTLs for gamma distributions. ProUCL 4.0, also have some 

bootstrap methods to compute UTLs. 

3.4.4 Nonparametric Upper Tolerance Limits 

The computation of nonparametric UTLs is somewhat messy as it is based upon binomial cumulative 

probabilities and order statistics. Just like parametric UTLs, a nonparametric UTL providing coverage to 

p100% observations with confidence coefficient (CC) (1 – α)100% represents an (1 – α)100% upper 

confidence limit for the p

th percentile of the population under study. Also, the nonparametric UTLs (given 

by order statistics) cannot exactly achieve the specified confidence coefficient, (1 – α). In most cases, 

only an approximate confidence coefficient can be achieved by nonparametric UTLs. One has to be 

satisfied with the achievable confidence coefficient, which is as close as possible to the specified CC of 

(1 – α). Thus, an appropriate UTL is chosen which provides coverage for the p

th percentile as close as 

possible to the specified confidence coefficient, (1 – α). Based upon binomial cumulative probabilities, 

the algorithms to compute nonparametric UTLs have been also incorporated in ProUCL 4.0 software. 

It is noted that for simplicity and based upon professional judgment, the 1992 RCRA addendum 

recommends the use of either the largest value or the second largest value for both UPL and UTL. 

However, the use of the largest value as UPL (or UTL) may result in an overestimate, especially when 

there is a possibility of the presence of potential outliers in the background data set. Therefore, to be 

protective of the human health and the environment, it is preferable to use the second largest (or even a 

lower order statistic) data value as a nonparametric UTL or UPL. Whenever, these higher order statistics 

79 
represent potential outliers (e.g., well separated from the majority of the data on a normal Q-Q plot), one 

should avoid their use as estimates of the BTVs. The selection of these higher (e.g., largest, second 

largest) order statistics (as estimates of BTVs) also depends upon the sample size of the background data 

set. Specifically, for smaller data sets, it is likely that the higher order statistics (largest, or the second 

largest) can be chosen to estimate the BTV. As mentioned earlier, in practice, a few high observations 

(outliers) may represent contaminated observations (e.g., well separated from the majority of the data on a 

normal Q-Q plot) and their selection should be avoided as nonparametric estimates of the BTVs. 

It is also noted that the largest value is also used as an estimate of the BTV when a 95% UTL (especially

for lognormal skewed data sets) exceeds the largest value in a data set. Moreover, when a background 

UTL does exceed the maximum value in the background data set, it is quite likely that the maximum 

value may represent an extreme value (perhaps from some impacted site area). The use of maximum

value can be avoided by using appropriate upper percentiles (e.g., 95%) described earlier as estimates of 

background values. 

A brief description of the computation of nonparametric UTLs (confidence intervals for percentiles) now 

follows. For details, the interested readers are referred to David and Nagaraja (2003), Conover (1980), 

and Hahn and Meeker (1991). The binomial distribution is used with the number of trials = the sample 

size, n, and the probability of success = p (the proportion of observations for which coverage is being 

sought). Using the cumulative binomial probabilities, a number, r: 1 ≤ r ≤ n, is chosen such that the 

cumulative binomial probability:

i=

∑

r 

⎜

⎜

⎛n 

⎟

⎟

⎞

p

i (1− p)

(n−i) becomes as close as possible to (1 – α). Then, the r

th order statistic, x(r) is picked as

i=0 ⎝i ⎠

the (1 – α)100% UTL providing coverage to p100% of the observations in the sample under study 

(background data set here). This algorithm has been incorporated in ProUCL for samples of sizes up to 

2000. ProUCL 4.0 also prints out the order statistic, the estimate of the UTL, and the associated coverage 

provided (achieved) by that order statistic. For larger samples, one can use the following approximation, 

which has also been incorporated in ProUCL. For n > 2000, a large sample normal approximation to the 

binomial distribution can be used to obtain an upper confidence limit for the p

th percentile. The number, r: 

1 ≤ r ≤ n, used to compute the r

th order statistics (to estimate the BTV) is obtained using the following 

equation: 

r = np + z(1−α ) np(1− p) + 0.5 (3-8) 

3.4.5 Example 2: Computation of Upper Tolerance Limits 

Using the background data set used earlier associated with a Superfund site, the computation of the 

various parametric and nonparametric upper tolerance limits have been illustrated in this example. To 

illustrate the differences in the values of the UTLs as a function of the coverage coefficient, p%, UTLs 

have been computed for four (4) different coverage coefficients, p% = 80%, 90%, 95%, and 99%. 

3.4.5.1 Normal Upper Tolerance Limits 

As noted earlier (Figure 3-1), aluminum concentrations follow a normal distribution. Therefore, the 

various normal UTLs with coverage coefficients of 80%, 90%, 95%, and 99% are listed as follows. 

80 
95% UTL (80% coverage) = x + K(24,0.95,0.80) * s = 7789.1667 + 1.331 * 4263.8969 = 13387.663 

95% UTL (90% coverage) = x + K(24,0.95,0.90) * s = 7789.1667 + 1.853 * 4263.8969 = 15690.168 

95% UTL (95% coverage) = x + K(24,0.95,0.95) * s = 7789.1667 + 2.309 * 4263.8969 = 17634.505 

95% UTL (99% coverage) = x + K(24,0.95,0.99) * s = 7789.1667 + 3.181 * 4263.8969 = 21362.623 

3.4.5.2 Lognormal Upper Tolerance Limits 

As noted earlier, chromium background concentrations follow a lognormal distribution. The 95% UTLs 

based upon a lognormal distribution are given as follows. 

95% UTL (80% coverage) = exp( y + K(24,0.95,0.80) * s y ) = exp(2.3344 + 1.331 * 5678) = 21.7583 

95% UTL (90% coverage) = exp( y + K(24,0.95,0.90) * s y ) = exp(2.3344 + 1.853 * 5678) = 29.5659 

95% UTL (95% coverage) = exp( y + K(24,0.95,0.95) * s y ) = exp(2.3344 + 2.309 * 5678) = 38.3039 

95% UTL (99% coverage) = exp( y + K(24,0.95,0.99) * s y ) = exp(2.3344 + 3.181 * 5678) = 62.8464 

3.4.5.3 Nonparametric Upper Tolerance Limits 

Earlier, it was determined that the background lead concentrations do not follow any of the known 

distributions as incorporated in ProUCL 4.0. For lead, 95% UTLs based upon binomial cumulative 

probabilities and order statistics are given in Table 3-2. It should be noted that the resulting UTL might 

not achieve the exact specified CC of 0.95. 

Following the procedure described earlier, a 95% UTL with coverage coefficient of 80% is represented by 

the 22nd order statistic. The resulting UTL (22nd order statistic) covers about 80% of the observations (that 

is 80% observations are ≤ x(22) ) with a probability (confidence coefficient) of 0.967 (instead of 0.95). A 

95% UTL with a coverage coefficient of 90% is represented by the 23rd order statistic. The resulting UTL 

(23rd order statistic) covers about 90% of the observations (that is 90% observations are ≤ x(23) ) with a 

probability (confidence coefficient) of 0.922 (instead of 0.95). Using order statistics, the actual achieved 

confidence, (1 - α) is often different from the user requested confidence coefficient of 95%. ProUCL 4.0 

selects the order statistic that achieves the confidence coefficient closest to the user specified confidence 

coefficient. 

Table 3-2. Nonparametric Upper Tolerance Limits for Lead 

Coverage Order Statistic Achieved 

Coefficient (p%) Order Value Confidence (1 – α)

80% x(22) 53.3 96.7% 

90% x(23) 98.5 92.2% 

95% x(24) 109 100% 

99% x(24) 109 100% 

Caution: Since nonparametric UTLs are given by order statistics, every effort should be made to make 

sure that the chosen order statistic to estimate the BTV does not represent an outlying observation coming 

from a population other than the background population. 

81 
3.5 Nonparametric Upper Limit Based Upon Interquartile Range (IQR) – IQR 

Upper Limit 

Sometimes, an upper limit based upon the IQR of the background data set is used as an estimate of the 

BTV. In this chapter, we denote this limit by the IQR Upper Limit. It is very simple to compute and is 

briefly described below. The simple formula to compute IQR Upper Limit is: 

IQR Upper Limit = Q3+ 1.5* IQR. (3-9) 

Here IQR = Q3 – Q1, the interquartile range, the difference between the third (upper) quartile, Q3, and 

the first (lower) quartile, Q1, of the background data set. The quartiles of a data set are defined in most 

applied statistical books (e.g., Hoaglin, Mosteller and Tukey, 1983). The three quartiles, Q1, Q2, and Q3 

of a data set divide the data set into four (4) equal parts. Note that the second quartile represents the 

median of the data set. Thus, 25% of the data lie at or below Q1, 50% of the data lie at or below Q2 

(median), and 75% of the data lie at or below Q3; therefore, 25% of the data lie above Q3. Just like all 

other limits described in this chapter, individual site observations are compared with the IQR Upper 

Limit. Any site concentration exceeding the background level IQR Upper Limit may be considered 

justification to consider contamination at the site. The computation of IQR Upper Limit has also been 

incorporated in ProUCL 4.0 software package. 

Note: The behavior of an IQR-based limit as an estimate of a BTV is not well studied. Therefore, this limit 

should be used with caution to estimate the BTVs or not-to-exceed values. 

3.5.1 Example 3: IQR Upper Limit 

Sometimes, in practice, a nonparametric upper limit based upon the interquartile range (IQR) of the data 

set under study is used as an estimate of the background threshold value. Since lead does not follow any 

of the parametric distributions as incorporated in ProUCL, an upper limit based upon the IQR can be used 

as an estimate of the BTV. This will require the use of the first quartile, Q1, and the third quartile, Q3. 

Here Q1 = 8.7, Q3 = 19, and 

IQR = Q3 - Q1 = 10.3 

An estimate of the BTV based upon IQR is given as follows. 

IQR Upper Limit = Q3 + 1.5 * IQR = 19 + 1.5 * 10.3 = 34.45 

3.6 Upper Prediction Limits 

As mentioned before, both the 1989 RCRA document and its 1992 addendum suggest the use of upper 

prediction limits (UPLs) as estimates of background level threshold values. If the background and site 

contaminant distributions are comparable, then a typical site observation should lie below a 95% UPL 

based upon a background data set with probability 0.95. A site observation exceeding the background 

95% UPL can be considered as providing some evidence of contamination due to site related industrial 

activities. Since a UPL does represent a plausible way of expressing background level contaminant 

concentration, a brief discussion of both parametric as well as nonparametric UTLs is presented in this 

section. 

82 
3.6.1 Normal Upper Prediction Limit 

The sample mean, x , and the standard deviation (sd), s, are computed first based upon a defensible 

unique (e.g., outliers, multiple populations, mixture populations not allowed) background data set without 

the outliers. For normally distributed data sets, an upper (1 – α)100% prediction limit is given by the 

following well known equation: 

UPL = x + t (1+1/ n) (3-10) ((1−α ),(n−1)) * s * 

Here t((1−α ),(n−1)) is a critical value from Student’s t-distribution with (n–1) degrees of freedom. 

3.6.2 Lognormal Upper Prediction Limit 

An upper (1 – α)100% lognormal UPL is similarly given by the following equation: 

UPL = exp( y + t((1−α ),(n−1)) * s y * (1+1/ n)) (3-11) 

Here t((1−α ),(n−1)) is a critical value from Student’s t-distribution with (n–1) degrees of freedom. 

3.6.3 Nonparametric Upper Prediction Limit 

As mentioned before, the background data set under consideration should represent a single population, 

and should be free of outlying observations, which may represent data from impacted areas of the site. A 

one-sided nonparametric UPL is simple to compute and is given by the following mth order statistic. One 

can use linear interpolation if the resulting number, m, given below does not represent a whole number (a 

positive integer). 

UPL = X(m), where m = (n + 1) * (1 – α). (3-12) 

For example, for a nonparametric data set of size 25, a 90% UPL is desired. Then m = (26*0.90) = 23.4. 

Thus, a 90% nonparametric UPL can be obtained by using the 23rd and the 24th ordered statistics and is 

given by the following equation: 

UPL = X(23) + 0.4 * (X(24) - X(23) ) 

Similarly, if a nonparametric 95% UPL is desired, then m = 0.95 * (25 + 1) = 24.7, and a 95% UPL can 

be similarly obtained by using linear interpolation between the 24th and 25th order statistics. However, if a 

99% UPL needs to be computed, then m = 0.99 * 26 = 25.74, which exceeds 25, the sample size. 

Therefore, for such cases, the highest order statistic (the largest value) has to be used as the 99% UPL of 

the background data set under study. The largest value(s) should be used with caution (as they may 

represent outliers) to estimate the BTVs. 

3.6.4 Example 4 

The same background data set as used earlier has been used in this example. All computations have been 

carried out using ProUCL 4.0. 

83 
3.6.4.1 Normal Upper Prediction Limit 

As noted earlier, the aluminum concentrations in our example do follow a normal distribution. A 95% 

UPL for aluminum is given as follows. 

UPL = x + t((1−α ),(n−1)) * s * (1+1/ n) = 7789.17 + 1.7139 * 4.263.90 * 1.02 = 15247.628 

3.6.4.2 Lognormal Upper Prediction Limit 

The chromium background concentrations of Example 1 follow a lognormal distribution. A 95% UPL for 

chromium is given by the following equation. 

UPL = exp( y + t((1−α ),(n−1)) * s y * (1+1/ n)) = exp(2.3344 + 1.7139 * 0.5678* 1.02) = 27.8738 

3.6.4.3 Nonparametric Upper Prediction Limit 

A nonparametric UPL can be computed using the following equation: 

UPL = X(m), where m = (n + 1) * (1 – α). (3-13) 

For lead concentrations, with n = 24 and (1 -α) = 0.95, the corresponding 95% UPL is given by the m

th = 

23.75thorder statistic, which can be computed using simple linear interpolation as follows: 

X(23.75) = X(23) + 0.75(X(24) - X(23) ) = 98.5 + 0.75(10.5) = 106.375. 

Note: As mentioned before, nonparametric UPLs (and also UTLs) are typically represented by higher 

order statistics, or by some value in between (based upon linear interpolation) the higher order statistics. 

If those higher order statistics represent contaminated outlying observations, then a value lying between 

the two contaminated observations will also be an outlier. For example for lead, if the two high 

observations: 98.5 and 109 are considered as outliers, then the 95% UPL = 106.375 as computed above 

will also represent an outlier. 

Therefore, nonparametric UTLs or UPLs should be used with caution to estimate the BTVs. Every effort 

should be made to identify and separate the outlying observations before computing nonparametric limits 

to estimate the BTVs. 

For the comparison sake, the 95% UPLs for aluminum, arsenic, chromium, and lead as produced by 

ProUCL (irrespective of the data distribution) have been summarized in Table 3-3. 

84 
Table 3-3. 95% Upper Prediction Limits for Selected Contaminants 

Inorganic Contaminants 

Distribution Aluminum Arsenic Chromium Lead 

Normal 15247.63 4.1764 24.022 69.418 

Lognormal 19245.46 4.6277 27.874 59.784 

non-discernable 16000.00 5.3500 31.625 106.375 

Figure 3-5. Shapiro-Wilk Normal Distribution Test for Lead 

Figure 3-6. Shapiro-Wilk Lognormal Distribution Test for Lead 

85 
86 
Figure 3-7. All Background Statistics for Lead 

87 
88 
Chapter 4 

Computation of Upper Confidence Limit of the Population Mean 

Based Upon Data Sets with Nondetect Observations 

4.1 Introduction 

Nondetects (NDs) or below detection limit (BDL) observations are inevitable in most environmental data 

sets. Singh, Maichle, and Lee (EPA, 2006) studied the performances of the various parametric and 

nonparametric UCL computation methods for data sets with NDs and multiple detection limits. This 

chapter represents a sub-chapter of Singh, Maichle, and Lee (EPA, 2006) summarizing their main 

findings and recommendations. Based upon their findings and results, some of the better (in terms of 

coverage probabilities) performing UCL computation methods have been incorporated in ProUCL 4.0. 

Specifically, the new UCL module of ProUCL 4.0 can be used to compute UCLs of the population means 

based upon left-censored data sets (with single and multiple detection limits) from normal, lognormal, and 

gamma distributions. The details of those methods can be found in Singh, Maichle, and Lee (EPA, 2006). 

The estimation of mean and standard deviation (sd) based upon data sets with NDs have been studied by 

many researchers (Cohen, 1991, Gibbons and Coleman, 2001, Schneider 1986, Gilliom and Helsel, 1986, 

Singh and Nocerino, 2002, and Helsel, 2005). However, it is noted that not much guidance is available on 

how to accurately compute the various upper limits (e.g., UCLs, UPLs, UTLs) often used in 

environmental applications. For example, in many exposure and risk assessment applications, one needs 

to compute a 95% upper confidence limit (UCL) of the mean based upon data sets with BDL 

observations. 

Also, in several background evaluation studies (e.g., estimating background threshold values (BTVs)), 

one needs to compute upper prediction limits (UPLs), upper percentiles, and upper tolerance limits 

(UTLs) using left-censored data sets. Statistical methods to compute upper limits (UPLs, UTLs) based 

upon data sets with BDL observations are described in Chapter 5 of this Technical document. In the 

recent environmental literature (e.g., Millard 2000 and USEPA-UGD 2004), the use of some ad hoc “ruleof-thumb” type methods, based upon the Student’s t-statistic or Land’s H-statistic has been suggested to 

compute the 95% UCLs, 95% UTLs, and 95% UPLs. For example, it is noted that the Student’s t-statistic 

(on Cohen’s maximum likelihood estimates) is used to compute the upper prediction limit (pages 10-26, 

USEPA-UGD (2004)) without justifying its use. It should be noted that the distribution of the statistic 

used to construct the UPL (pages 10-26, USEPA-UGD (2004)) is unknown as it is based upon maximum 

likelihood estimate (MLE) of the mean and standard deviation, which are very different from the 

traditional mean and standard deviation used in the definition of a typical Student’s t-statistic. This “ruleof-thumb” method will be even harder to defend for skewed data sets with standard deviation of logtransformed data exceeding 1.0. 

Let x1, x2, ..., xn (including NDs and detected measurement) represent n data values obtained from samples 

collected from an area of concern (AOC) or some other population of interest (e.g., reference area). It is 

assumed that k : 1≤k≤n, observations lie below one or more detection limit(s), and the remaining (n - k) 

observations represent detected observations, which might come from a well-known parametric 

distribution such as a normal, a lognormal, or a gamma distribution, or from a population with a nondiscernable distribution. Since multiple detection limits are allowed, some of the detected data may be 

smaller than the detection limit(s) associated with ND observations. 

89 
Note: It should be noted that it is not easy to verify the distribution of left-censored data sets, especially 

when a large percentage (> 40% -50%) of observations are being censored (nondetected). In such cases, 

it is desirable to use nonparametric methods to compute various statistics (upper limits, hypotheses 

testing statistics) of interest. Several nonparametric methods to compute various upper limits based upon 

data sets with ND observations are available in ProUCL 4.0. 

ProUCL 4.0 also provides some ad hoc goodness-of-fit (GOF) methods to verify the data distribution 

based upon quantile-quantile (Q-Q) plot of the (n - k) detected observations supplemented with the 

available GOF test (e.g., as in ProUCL 3.0) statistics computed using the detected observations. This 

chapter also discusses the construction of graphical displays such as Q-Q plots that may be used to 

determine the distribution of data sets with ND observations. These methods have been incorporated in 

ProUCL 4.0. Additionally, using regression on order statistics (ROS) methods for normal, lognormal, or 

gamma distributions, one can extrapolate and estimate the NDs. This process yields a full data set of size 

n with (n-k) original detected observations, and k extrapolated nondetects. ProUCL 4.0 has the capability 

to store these data (k estimated and (n-k) original detected) in new columns (e.g., for use in other 

applications) generated by ProUCL 4.0. This option is available in ProUCL 4.0 for more advanced users. 

Some important notes and comments that apply to data sets with and without outliers are described as 

follows. 

Note on skewness: In ProUCL 3.0 and also in ProUCL 4.0, for skewed distributions such as a lognormal 

distribution (or some non-discernable distribution), skewness is measured as a function of sd, σ (or its 

estimate, σˆ ) of log-transformed data. Also, it should be noted out that sd, σ, or its estimate, σˆ of logtransformed detected data is used to get an idea about the skewness of a data set with ND observations. 

This information is useful to decide which UCL should be used to estimate the EPC term for data sets 

with and without ND observations. For data sets with ND values, output spreadsheets generated by 

ProUCL 4.0 do exhibit the sd,σˆ , of log-transformed data based upon detected observations. For gamma 

distribution, skewness is a function of the shape parameter, k. Therefore, in order to assess the skewness 

of gamma distributed data sets, the associated output screens exhibit the MLE of the shape parameter, k 

based upon detected observations. 

4.2 Pre-processing a Data Set 

Throughout this chapter (and in other chapters such as Chapters 2, 3, and 5), it has been implicitly 

assumed that the data set under consideration represents a “single” population. Specifically, it assumed 

that the user had pre-processed the data by performing preliminary and exploratory data analyses. This 

means that the user has separated out data sets from a potentially mixture sample (if any) into component 

sub-samples representing single individual population(s). The user may want to consult a statistician to 

perform this kind of population partitioning on a mixture sample. Some of exploratory graphical methods 

are also available in ProUCL 4.0 

4.2.1 Handling of Outliers 

The main objective of using a statistical procedure is to model the majority of the data representing the 

main dominant population, and not to accommodate a few outliers that may yield inflated and impractical 

results. The background values should be estimated by reliable statistics (and not distorted statistics) 

obtained using data sets representing the dominant background population. High outlying values 

contaminate the underlying left-censored or uncensored full data set from the population under study. In 

practice, it is the presence of a few extreme outliers that cause the rejection of normality of a data set 

(Barnett and Lewis (1994)). 

90 
4.2.2 Disposition of Outliers 

One can argue against “not using the outliers” while estimating the various environmental parameters 

such as the EPC terms and BTVs. An argument can be made that the outlying observations are inevitable 

and can be naturally occurring (not impacted by site activities) in some environmental data sets. For an 

example, in groundwater applications, a few elevated values (occurring in tails of data distribution with 

lower probabilities) are naturally occurring, and as such may not be representing the impacted and 

contaminated monitoring well data values. Those data values originating from the groundwater studies 

may require separate investigation, and all interested parties should decide on how to deal with data sets 

that contain naturally occurring unimpacted outlying observations. The entire project team should decide 

about the appropriate disposition of such outlying observations. The project team should come to an 

agreement whether to treat the outlying observations separately or to include them in the computation of 

the various statistics of interest such as the sample mean, standard deviation, and the associated upper 

limits. 

4.2.3 Assessing Influence of Outliers 

In order to assess the influence of outliers on the various statistics (e.g., upper limits) of interest, it is 

suggested to compute all relevant statistics using data sets with outliers and without outliers, and compare 

the results. This extra step often helps to see the direct potential influence of outlier(s) on the various 

statistics of interest (e.g., mean, UPLs, UTLs). This in turn will help the project team to make informative 

decisions about the disposition of outliers. That is, the project team and experts familiar with the site 

should decide which of the computed statistics (with outliers or without outliers) represent better and 

more accurate estimate(s) of the population parameters (e.g., mean, EPC, BTV) under consideration. 

Since the treatment and handling of outliers is a controversial and subjective topic, it is suggested that the 

outliers be treated on a site-specific basis using all existing knowledge about the site and the site 

background (e.g., EA, area of concern (AOC), reference area) under investigation. 

4.2.4 Log-Transformation Tends to Accommodate Outliers (and Contamination) 

It should also be noted that often in practice, the use of a log-transformation on a data set tends to 

accommodate outliers and hide contaminated locations instead of revealing them. Specifically, an 

observation that is a potential outlier (representing a contaminated location) in the original raw scale may 

not seem to be an outlier in the log-scale. Once again since the cleanup and remediation decisions have to 

be performed using data and statistics in original scale, the use of a log-transformation should be avoided 

to achieve symmetry and normality. Instead, the use of nonparametric methods is preferable. 

4.2.4.1 Avoid Data Transformations 

It is suggested to avoid the use of transformations (Singh, Singh, Engelhardt (1997) and Singh, Singh, and 

Iaci (2002)) of the raw data to achieve symmetry (approximate normality). In most environmental 

applications, the cleanup decisions have to be made on statistics and results computed in the original 

space/scale as the cleanup goals need to be attained in the original scale. Therefore, statistics and results 

need to be back-transformed in the original scale before making any cleanup decisions. Furthermore, the 

parameter (hypothesis of interest) in the transformed space is not of interest to make remediation and 

cleanup decisions. Often, the back-transformed estimates of the parameters (from transformed space) in 

the original space suffer from a significant amount of transformation bias (e.g., see the results of the backtransformation of ROS estimates or MLE obtained using log-transformed data in Singh, Maichle, and 

Lee, 2006). Many times the transformation bias can be unacceptably large (for highly skewed data sets) 

and unreasonable, leading to incorrect decisions. Therefore, AVOID the use of transformations. 

91 
It is recommended to avoid the use of equation (4-2) as given below (Shaarawi, 1989) to back-transform 

estimates from log-scale to original scale. The question now arises – how one should back-transform 

results from a log-space (or any other transformed space) to the original space. Unfortunately, no 

defensible guidance or procedure is available in the literature to address this issue. Moreover, the backtransformation formula will change from transformation to transformation (Box-Cox (BC)-type 

transformations), and the bias introduced by such transformations will remain unknown. This is one of the 

main reasons that ProUCL 4.0 does not compute MLE estimates (or other estimates such as fully 

parametric estimates using ROS on logged data) using log-transformed data. 

Therefore, in cases when a data set in the “raw” scale cannot be modeled by a parametric distribution, it is 

desirable to use nonparametric methods rather than testing or estimating some parameter(s) in the 

transformed space. This is especially true when dealing with data sets with nondetect observations. 

4.2.4.2 Do Not Use DL/2 (t) UCL Method 

The DL/2 goodness of fit tests and UCL computation methods are included for historical reasons. It is 

suggested that the use of DL/2 (t) method should be avoided to estimate the EPC term or other threshold 

values. This UCL computation method does not provide adequate coverage (for any distribution and 

sample size) for the population mean, even for censoring levels as low as 10%, 15%. This is contrary to 

the conjecture and assertion (e.g., EPA (2006)) often made that the DL/2 method can be used for lower (≤

20%) censoring levels. The coverage provided by the DL/2 (t) method deteriorates fast as the censoring 

intensity increases. 

4.2.5 Minimum Data Requirement 

If the use of appropriate data quality objectives (DQOs) (e.g., USEPA (2006)) is not possible (e.g., data 

might have been already collected), every effort should be made to collect a data set with about 8-10 

detected observations to compute reasonably reliable estimates of EPC terms (UCLs) and BTVs (e.g., 

UPLs, UTLs). Whenever possible, it is desirable to collect more detected observations, especially when 

the percentage of NDs becomes greater than 40%, 50%, and so on. It should also be noted that the use of 

the minimum of 10 to 15 detected observations is desirable to compute UCLs and other upper limits 

based upon re-sampling bootstrap methods. These issues have also been discussed in Chapter 1 of this 

Technical Guide. 

Most of the comments and notes described above apply to all data sets with and without nondetect 

observations. 

4.3 Estimation of Population Mean and Variance Based Upon 

Left-Censored Data Sets 

For left-censored data sets with NDs, the detailed description of the various methods to estimate the 

population mean, μ, and sd, σ, including the: MLE (Cohen, 1950, 1959) method, restricted MLE (RMLE) 

method (Person and Rootzen, 1977), expectation maximization (EM) method (Dempster, Laird, and 

Rubin, 1977, Gleit, 1985), EPA Delta lognormal method (EPA, 1991), Winsorization (Dixon and Tukey 

1968, Gilbert, 1987) method, regression method (Newman, Dixon, and Pinder, 1989)), regression on 

order statistics (ROS) method (Gilliom and Helsel 1986, Helsel, 1990), and Kaplan-Meier (KM) method 

(1958) can be found in Singh and Nocerino (2001), and Singh, Maichle, and Lee (2006). Only two 

estimation methods, namely the ROS (normal, lognormal, and gamma) methods, and KM method are 

92 
described in this section. These estimation methods can be used on data sets with multiple detection 

limits. 

The Q-Q plots and the GOF statistics based upon detected observations, and also based upon the data set 

consisting of detected observations and extrapolated (estimated) NDs obtained using ROS methods can be 

used to determine the data distribution of the left-censored data set under study. It should be noted that, it 

is not easy to verify the distribution of left-censored data sets, especially when a large percentage (> 40%­

50%) of observations are being censored (nondetects). Therefore, it is preferable to use nonparametric 

methods to compute statistics of interest using data sets with ND observations. 

For data sets with NDs, ProUCL 4.0 does provide some simple GOF methods based upon the ROS 

methods. Those simple ad hoc methods to verify the data distribution based upon the quantile-quantile 

(Q-Q) plot of the (n - k) detected observations supplemented with the available GOF test statistics 

computed using the detected observations are briefly described in the following sections. 

4.3.1 Goodness-of-Fit (GOF) Tests for Data Sets with Nondetect Observations 

Throughout this chapter, let x1, x2, ... , xn (including NDs and detected measurements) represent a random 

sample of n observations obtained from a population under investigation (e.g., background area, or an 

AOC). Out of the n observations, k: 1≤k≤n, values are reported as nondetects lying below one or more 

detection limits, and the remaining (n-k) observations represent the detected data values. The (n-k) 

detected values are ordered and are denoted by x(i) ; i:=k+1, k+2,...n. The k nondetect observations are 

denoted by x(ndi) ; i:=1,2,...k. These k nondetect values can be predicted or extrapolated using a ROS 

method. The full data set of size n thus obtained may be used to compute the various summary statistics, 

and to estimate the EPC terms, BTVs, and other not-to-exceed values using methods described in 

Chapters 2 and 3 of this Technical guidance document. Several ROS methods have been cited and used in 

the literature for left-censored data sets (e.g., Helsel (2005)) with ND observations. However, it is pointed 

out that the use of ROS methods often yields infeasible and negative estimates of ND observations. This 

is especially true when outliers may be present in a data set (Singh and Nocerino, 2002). A description of 

ROS methods as incorporated in ProUCL 4.0 is given as follows. 

It should be noted that the ROS estimation methods are parametric in nature as they involve extrapolation 

of the nondetects based upon certain distributional assumptions about the detected as well as nondetected 

observations. In this process, nondetects are imputed (extrapolated) based upon the assumed distribution 

(e.g., normal, lognormal, or gamma) of the detected observations. Using the menu option, “ROS Est. 

NDs”, ProUCL 4.0 can be used to store the extrapolated NDs along with the original detected values in 

additional columns generated by ProUCL 4.0. ProUCL 4.0 assigns suitable and self-explanatory titles for 

those generated columns. 

One of the objectives here is to determine the data distributions data sets consisting of detected and 

nondetected observations. An ad hoc method to verify the data distribution is based upon the quantile - 

quantile (Q-Q) plot of the (n - k) detected observations supplemented with the available goodness-of-fit 

test statistics (e.g., S-W test, A-D test, and K-S test) computed using the detected observations. Methods 

described below (and incorporated in ProUCL 4.0) may be used on left-censored data sets with a single 

detection limit as well as multiple detection limits. Some of these methods such as ROS method can also 

handle cases when some of the detection limits (nondetects) exceed the observed detected values. 

93 
4.3.2 Regression on Order Statistics (ROS) Estimation Methods 

Typically, in a ROS method, an ordinary least squares (OLS) regression model is obtained first by fitting 

a linear straight line to the (n-k) detected values, x(i) (perhaps after a suitable transformation) and the (n-k) 

hypothesized (e.g., normal, gamma) quantiles, q(i); i:=k+1, k+2,...,n associated with those (n-k) detected 

observations. It should be noted that the hypothesized quantiles are obtained for all of the n data values by 

using a specified distribution such as a normal or a gamma distribution. The (n-k) quantiles associated 

with (n-k) detected values are denoted by q(i); i:=k+1, k+2,...,n, and the k quantiles associated with 

nondetect observations are denoted by q(ndi); i:= 1, 2, ..., k. The fitted linear model based upon (n-k) pairs 

is then used to predict or extrapolate the k nondetect observations. Obviously, in order to obtain a reliable 

model (slope and intercept) and extrapolated NDs, enough (at least 8-10) detected values should be 

available. 

When there is only a single detection limit (DL) and all values lying below DL represent nondetect 

observations, then the quantiles corresponding to those nondetect values typically are lower than the 

quantiles associated with the detected observations. However, a different quantile position (percentile) 

computation method (e.g., see Helsel (2005)) is used to accommodate multiple detection limits and cases 

when some of the detected values may be smaller than some of the detection limits. Therefore, when 

multiple detection limits are present, the quantiles associated with some of the nondetect observations 

may exceed the quantiles associated with detected data values. 

As mentioned before, in a ROS method, it is assumed that the k censored (nondetect) observations, x1, x2, 

..., xk, follow a normal (or lognormal distribution when logged data are used), gamma, or some other 

distribution. Before computing the n hypothesized quantiles, q(i); i:=k+1, k+2,...,n, and q(ndi); i:= 1, 2, ..., 

k, the plotting positions (also known as percentiles) need to be computed for all of n observations with k 

nondetects and (n-k) detected values. There are many methods available in the literature (Blom (1958), 

Johnson and Wichern (2002), Helsel (2005)) to compute the appropriate plotting positions (percentiles). 

Once the plotting positions (empirical percentiles) have been obtained, the associated quantiles are 

computed based upon the hypothesized distribution (e.g., normal, gamma) that needs to be tested. A few 

plotting position (percentile) computation methods as incorporated in ProUCL 4.0 are described in this 

section. 

Once the n plotting positions (empirical probabilities, percentiles) have been computed, the n quantiles, 

q(ndi); i:= 1, 2, ..., k, and q(i); i:=k+1, k+2,...,n are computed using the specified distribution (e.g., 

normal, gamma) corresponding to those n plotting positions. As mentioned before, when there are 

multiple detection limits, and when some of those detection limits exceed the detected values, then 

quantiles, q(ndi) corresponding to some of those nondetect values might become greater than the quantiles, 

q(i) associated with some of the detected values. The resulting quantiles when used to obtain an OLS 

regression often yield extrapolated nondetect values exceeding some of the detected values. Some of 

these issues are illustrated by examples in Chapter 6 of the revised Guidance for Comparing Background 

and Chemical Concentrations in Soil for CERCLA Sites (EPA, 2002b). 

4.3.3 OLS Regression Line Based Upon Detected Data 

An ordinary least squares (OLS) regression line is obtained using the (n - k) pairs, (q(i), x(i)); i:= k + 1, k + 

2, ..., n, where x(i) are the (n-k) detected values arranged in ascending order. The n quantiles, q(i) are 

computed using the hypothesized distribution (e.g., normal, gamma). The OLS regression line fitted to the 

(n - k) pairs (q(i), x(i)); i:= k + 1, k + 2, ..., n corresponding to the detected values is given by: 

94 
x(i) = a + bq(i); i:= k + 1, k + 2, ..., n. (4-1) 

It should be noted that in (4-1) above, the hypothesized quantile q(i) is associated with the detected value 

x(i); i:=k+1, k+2,...n. When ROS is used on transformed data (e.g., log-transformed data), then ordered 

values, x(i) ; i: = k + 1, k + 2, ..., n represent ordered detected data in that transformed scale (e.g., log 

scale, Box-Cox (BC)-type transformation). Equation (4-1) is used to predict or extrapolate the nondetect 

values in original or log-transformed space. Specifically, for quantile, q(ndi) corresponding to the ith 

nondetect, the extrapolated nondetect is given by x(ndi) = a + bq(ndi) ; i:=1,2,...k. As mentioned before, some 

of the predicted nondetect values thus obtained may become negative, or may exceed some of the 

detection limits and detected values. 

4.3.4 Computation of the Plotting Positions (Percentiles) and Quantiles 

Before computing the hypothesized (e.g., normal, gamma) quantiles, one has to compute the percentiles 

or plotting positions for the detected as well as nondetect observations. Those plotting positions are then 

used to compute the quantiles based upon the distribution used (e.g., normal, gamma). Several methods 

are available in the literature (e.g., Johnson and Wichern (2002), Helsel (2005)) that can be used to 

compute the n percentiles or n plotting positions. 

For a data set of size n, the most commonly used plotting positions for the ith observation (ordered) are 

given by (i – 3⁄8) / (n + 1⁄4) or (i – 1⁄2)/n. Typically, these plotting positions are used when one wants to 

graph a Q-Q plot of full-uncensored data set or wants to use ROS method on a data set with a single 

detection limit, DL with all nondetect values lying below the detection limit DL. For the single DL case 

(with all of the nondetect observations below the DL), ProUCL 4.0 uses percentiles, (i – 3⁄8) / (n + 1⁄4) for 

normal and lognormal distribution, and uses empirical percentiles given by (i – 1⁄2)/n for a gamma 

distribution. 

Once, the plotting positions have been obtained, the n normal quantiles, q(i) are computed using the 

probability statement: P(Z ≤ q(i)) = (i – 3⁄8) / (n + 1⁄4), i : = 1, 2, ..., n , where Z represents a standard 

normal variable (SNV). The gamma quantiles are computed using the probability statement: P(X ≤ q(i)) = 

(i – 1⁄2) /n, i : = 1, 2, ..., n , where X represents a gamma random variable. The details of the computation 

of the gamma quantiles are also given in the ProUCL 3.0 Technical Guide (EPA, 2004). 

In case multiple detection limits are present, perhaps with some of the detected values smaller than some 

of the detection limits (or nondetects exceeding some of the detected values), the plotting positions 

(percentiles) are computed using appropriate methods that adjust for multiple detection limits. The details 

of the computation of such plotting positions (percentiles), pi; i: =1, 2, ... ,n, for data sets with multiple 

detection limits or with nondetect observations exceeding the detection limits are given in Helsel (2005). 

The associated hypothesized quantiles, q(i) are obtained by using the following probability statements: 

P (Z ≤ q(i)) = pi; i : = 1, 2, ..., n (Normal or Lognormal Distribution) 

P (X ≤ q(i)) = pi; i : = 1, 2, ..., n (Gamma Distribution) 

4.3.5 ROS Estimates Obtained Using Only Detected Observations 

For full data sets, Barnett (1976) used the intercept and the slope of the regression line to estimate the 

population mean and the standard deviation. Newman, Dixon, and Pinder (1989) followed a similar 

approach (using normal quantiles), and used the intercept and the slope of the OLS line given by (4-1) to 

estimate population mean, μ, and standard deviation, σ, using left-censored data sets. It is noted that these 

95 
estimates ignore all of the information contained in nondetect observations. Singh and Nocerino (2002) 

noted that the use of this method using only (n - k) detected values results in biased estimates of the mean 

and standard deviation. Therefore, this method is not a recommended method to compute summary 

statistics and various upper limits used in environmental applications. 

4.3.5.1 ROS Method for Normal Distribution 

Once the OLS regression model given by (4-1) has been obtained based upon the (n-k) detected 

observations and the associated hypothesized quantiles (e.g., normal, lognormal, or gamma), the k 

nondetect observations can be predicted or extrapolated using the linear model given by (4-1). The use of 

this process yields a full data set of n values with k imputed new values, and (n-k) detected original data 

values. One may then want to use any of the methods including the bootstrap resampling methods as 

described in Chapters 2 and 3 to estimate the EPC terms, BTVs, and other not-to-exceed values. 

It should be pointed out that the use of ROS methods often results in infeasible estimates of nondetect 

observations. Specifically, the imputed nondetects become negative (e.g., when using ROS on a normal 

distribution), larger than the detection limits, and even larger than the detected values. This is especially 

true when some outlying observations may be present in the data set. The occurrence of outliers distorts 

all statistics including the slope and intercept of the linear OLS fit, and all extrapolated nondetect values. 

The use of distorted predicted nondetect observations in turn yields biased estimates of the population 

mean (e.g., reference area), standard deviation, and all other statistics of interest including UPLs and 

UTLs. Therefore, the use of ROS method assuming a normal distribution is not recommended. 

In such situations with infeasible estimates of NDs, some subjective checks may be provided to modify 

the regression method: negative estimates of NDs may be replaced by DL/2, and the estimated nondetects 

greater than DL may be replaced by DL itself. The mean and variance are then computed using the 

replacement values. Singh and Nocerino (2002) studied this method in their simulation study and 

concluded that the modified regression method yields biased estimates of population mean and variance. 

Therefore, this modified ROS method for normal distribution (or for any other distribution) is not 

available in ProUCL 4.0. 

4.3.5.2 ROS Method for Lognormal Distribution 

For the ROS method on log-transformed data, the OLS model given by (4-1) is obtained using the logtransformed detected data and the corresponding normal quantiles. Let Org stand for the data in the 

original unit and Ln stand for the data in the natural log-transformed unit. Using the OLS linear model on 

log-transformed detected observations, the nondetects in transformed log-units are obtained by 

extrapolation corresponding to the k normal quantiles, q(ndi) associated with ND observations. 

Once the k nondetects have been estimated, the sample mean and sd can be computed using the backtransformation formula (Shaarawi, 1989) given by equation (4-2) below. This method is also known as 

fully parametric method (Helsel, 2005). The mean, μˆ Ln , and sd, σˆ Ln , are computed in log-scale using a 

full data set obtained by combining the (n - k) detected log-transformed data values and the k 

extrapolated nondetect (in log scale) values. Assuming, lognormality, El-Shaarawi (1989) estimated μ and 

σ by back-transformation using the following equations as one of the several ways of computing these 

estimates. Note that these estimates suffer from significant amount of transformation bias as can be seen 

in examples discussed in Singh, Maichle, and Lee (2006). The estimates given by equation (4-2) are 

neither unbiased nor have the minimum variance (Gilbert (1987)). Therefore, it is recommended to avoid 

96 
the use of this version of ROS method on log-transformed data (also called the fully parametric ROS 

method, Helsel, 2005) to compute UCL95 and various other statistics. 

2 2 2 2

μˆOrg = exp(μˆ Ln + σˆ Ln / 2) , and σˆOrg = μˆOrg (exp(σˆ Ln ) −1) (4-2) 

Note: As mentioned before, it is recommended to avoid the use of equation (4-2) as given in Shaarawi 

(1989) to back-transform estimates from log-scale to original scale. Often, the back-transformed 

estimates of the parameters (from transformed space) in the original space suffer from a significant 

amount of transformation bias (e.g., see the results of back-transformation of ROS estimates or MLEs 

obtained using log-transformed data in Singh, Maichle, and Lee, 2006). Many times the transformation 

bias can be unacceptably large (for highly skewed data sets) and unreasonable, leading to incorrect 

decisions. 

It is also noted that the same formula given by equation (4-2) above is used on other parametric estimates 

(e.g., MLE, EM, RMLE, robust MLE) obtained in log-scale (assuming a lognormal distribution) to backtransform them in the original scale. Therefore, the MLE and all other parametric estimates obtained 

using a lognormal distribution suffer from significant amount of back-transformation bias (e.g., Singh, 

Maichle, and Lee (EPA, 2006)). The use of such estimates obtained using some kind of backtransformation is not recommended. Instead, the use of nonparametric methods (e.g., KM and bootstrap 

methods) on data sets in the original scale is preferred to compute estimates of environmental parameters 

including BTVs and EPC terms. 

This is one of the main reasons that ProUCL 4.0 does not compute MLE estimates (or other estimates 

such as fully parametric estimates using ROS method or EM method on logged data) using logtransformed data, as those estimates do require back-transformation based upon equations given by (4-2) 

above. Therefore, in cases when a data set in the “raw” scale cannot be modeled by a parametric 

distribution, it is desirable to use nonparametric methods rather than testing or estimating some 

parameter(s) in the transformed space. This is especially true when dealing with data sets with nondetect 

observations. 

4.3.5.3 Robust ROS Method on Log-Transformed Data 

Robust ROS method is also performed on log-transformed data as described above. In this robust ROS 

method (Helsel, 2005), nondetect observations are first extrapolated (predicted) in log scale based upon a 

linear ROS model fitted to the log-transformed detected values and normal quantiles. The estimated 

nondetects are transformed back in the original scale by simple exponentiation. It should be noted that, 

the process of using the ROS method based upon a lognormal distribution and imputing NDs by 

exponentiation will not yield negative estimates for nondetect values. However, this process still may 

yield some infeasible nondetects with some of the NDs exceeding their respective detection or reporting 

limits, and the estimated NDs exceeding the detected values. In any case, this process yields a full data set 

of size n. Any of the methods as described in Chapters 2 and 3 (and available in ProUCL 4.0) can be used 

to compute estimates of EPC terms, BTVs, not-to-exceed, and all other statistics of interest. 

Using the “ROS Est. with NDs” Option of ProUCL 4.0, one can save and store the extrapolated NDs 

along with the original detected values in additional columns generated by ProUCL 4.0. ProUCL 4.0 

assigns suitable self-explanatory titles for those generated columns. 

97 
4.3.5.4 Gamma ROS Method 

Many positively skewed data sets follow a lognormal as well as a gamma distribution. Singh, Singh, and 

Iaci (2002) noted that gamma distributions are better suited to model positively skewed environmental 

data sets. For full data sets, it is observed that the use of a gamma distribution results in reliable and stable 

95% UCL values. It is also noted that in order to use a gamma distribution, there is no need to transform 

the data and back-transform the resulting statistics. However, one has to estimate the gamma shape and 

scale parameters before computing the gamma quantiles and estimating (predicting) the NDs using a 

gamma distribution for detected data values. This process may have some effect on the adequacy and 

accuracy of the estimated gamma quantiles (based upon estimated value of the shape parameter, k), and 

consequently on the accuracy of the extrapolated NDs. Just like all other distributions, outliers, when 

present, can distort all statistics including slope, intercept, extrapolated NDs, mean, sd, UCL95, UPLs, 

and percentiles. 

If a left-censored data set follows a gamma distribution (can be verified using goodness-of-fit tests in 

ProUCL using any of the two empirical distribution function (EDF) tests – the K-S test or the A-D test on 

detected data); then, those NDs can be extrapolated using the regression model based upon (n - k) pairs 

given by: ((n - k) gamma quantiles, ordered (n - k) detected observations). As in normally distributed leftcensored data sets, for gamma distributed left-censored data sets, one can fit a linear regression model to 

(n - k) pairs: ((n - k) gamma quantiles, (n - k) ordered detected values) as given by formula (4-1). In these 

(n-k) pairs, the gamma quantile, q(i) is associated with the i

th detected observations (arranged in ascending 

order). 

Specifically, let xnd1, xnd2, ..., xndk, xk+1, xk+2, ..., xn, be a random sample (with k ND observations and (n-k) 

detected values) of size n from the gamma distribution, G(k,θ). Note that some of the ND values may be 

greater than the detected values. It is pointed out once again that in the data spreadsheet for ProUCL 4.0, 

all NDs, or <DL values are entered as the respective DL value (multiple DLs are allowed); and a column 

of 0 (associated with a nondetect) and 1 (associated with a detected value) value is assigned to each such 

contaminant (variable) with ND values. The n plotting positions, pi; i:= 1,2,...,n are computed for each 

observation (detected and nondetected) using the methods (for single detection limit and multiple 

detection limit cases) as described earlier in this chapter. One should not get confused with k, the shape 

gamma parameter, which is different from k, the number of NDs as used above. 

Let x(k+1) ≤ x(k+2) ≤ ... ≤ x(n) represent the ordered detected values (some of the DLs may be lying in 

between these (n-k) ordered detected values). In order to compute n gamma quantiles associated with the 

n plotting positions (percentiles, empirical probabilities), one needs to estimate the gamma parameters, k 

and θ. For left-censored data sets, these parameters are estimated based upon (n-k) detected values. 

Obviously enough detected data (e.g., at least 8-10) values are needed to compute reliable estimates of k 

and θ. Let k

ˆ and θ

ˆ represent the maximum likelihood estimates (MLEs) of k and θ respectively. For 

details of the computation of MLEs of k and θ, refer to Singh, Singh, and Iaci (2002). Just like all other 

ROS methods, in order to be able to compute reliable estimates of the nondetects and the resulting upper 

limits, enough detected observations (> 8-10) should be available. 

The Q-Q plot for a gamma distribution is obtained by plotting the scatter plot of pairs (x0i , x(i) ) 

i := k+1, 2, ..., n, where k=number of nondetects. The n quantiles, x0i, are given by the equation, 

x0i = z0iθ

ˆ / 2; i := 1, 2, ..., n, where the quantiles z0i (already ordered) are obtained by using the inverse 

chi-square distribution given as follows. 

98 
z0i 

∫ f (χ 2

2 

k

ˆ )dχ 2

2 

k

ˆ = (i −1/ 2) / n; i := 1, 2, ..., n (Single DL Case) (4-3) 

0 

z0i 

∫ f (χ 2

2 

k

ˆ )dχ 2

2 

k

ˆ = pi ; i := 1, 2, ..., n (Multiple DL Case) (4-4) 

0 

2 In the above equation, χ 2k

ˆ represents a chi-square random variable with 2k

ˆ degrees of freedom (df), and 

pi are the plotting positions (percentiles) obtained using the process described in Singh, Maichle, and Lee 

(2006) and Helsel (2005). The process of computing plotting positions, pi , i:=1,2,...,n, for left-censored 

data sets with multiple detection limits has been incorporated in ProUCL 4.0. The program, PPCHI2 

(Algorithm AS91) from Best and Roberts (1975) has been used to compute the inverse chi-square 

percentage points, z0i, as given by the above equation. 

Using a linear regression model given by equation (4-1) fitted to the (n - k) pairs, (gamma quantiles 

associated with detected data, detected data), one can extrapolate the k NDs contained in the data set. This 

will yield a full data set of size n = k + (n - k). 

It is noted that in order to use gamma ROS method, one has to estimate the gamma shape and scale 

parameters before computing the gamma quantiles and estimating (predicting) the NDs using a gamma 

distribution for detected data values. This process may have some effect on the adequacy and accuracy of 

the estimated gamma quantiles (based upon estimated value of the shape parameter, k), and consequently 

on the accuracy of the extrapolated NDs. Obviously, in order to obtain reasonably reliable estimates of 

gamma parameters, enough (e.g., > 8-10, more are desirable) detected data should be made available. 

Note: On data sets obtained using a ROS method, any of the available methods (including bootstrap 

methods) for full data sets may be used to compute estimates of BTVs and not-to-exceed values, and all 

other statistics of interest. These ROS methods are specifically useful when the BTVs and not-to-exceed 

values are to be estimated by percentiles. However, when the BTVs need to be estimated using UPLs and 

UTLs for left-censored data sets, one may want to use direct bootstrap methods on left-censored data 

sets. 

The fitted OLS linear model as given by (4-1) based upon (n-k) pairs is used to predict or extrapolate the 

k nondetect observations. Therefore, in order to obtain a reliable model (slope and intercept) and 

extrapolated NDs, enough (at least 8-10) detected values should be available. 

It is important to point out that in practice, it is not reasonable or justifiable to assume or expect that the 

NDs should follow the same distribution as assumed for the detected values. This is especially true when 

multiple detection limits may be present, and the % of NDs may be quite high (e.g., > 40%). Additionally, 

ROS methods often yield infeasible predicted NDs such as negative values, or greater than the respective 

detection limits. 

It is also noted that in order to use a gamma distribution, there is no need to transform the data and backtransform the resulting statistics. However, one has to estimate the gamma shape and scale parameters 

before computing the gamma quantiles and estimating (predicting) the NDs using a gamma distribution 

for detected data values. This process may have some effect on the adequacy and accuracy of the 

estimated gamma quantiles (based upon estimated value of the shape parameter, k), and consequently on 

the accuracy of the extrapolated NDs. Just like all other distributions, outliers, when present, can distort 

all statistics including slope, intercept, extrapolated NDs, mean, sd, UCL95, UPLs, and percentiles. 

99 
4.4 Saving Extrapolated Nondetect Values Using ROS Est. with NDs Option of 

ProUCL 4.0 

Using this option, nondetects are imputed (extrapolated) based upon the assumed distribution (e.g., 

normal, lognormal, or gamma) of the detected observations. Using the menu “ROS Est. NDs,” ProUCL 

4.0 can be used to save and store the extrapolated NDs along with the original detected values in 

additional columns generated by ProUCL 4.0. ProUCL 4.0 assigns suitable self-explanatory titles for 

those generated columns. This is available in ProUCL 4.0 for advanced users who want to use full data 

sets consisting of detected and extrapolated nondetect observations for other applications (e.g., Analysis 

of Variance, Principal Component Analysis). 

4.5 Influence of Outliers on ROS methods 

This section briefly discusses the influence of potential outliers on the various statistics of interest 

including the UCLs, UPLs, and UTLs. Singh and Nocerino (2002) demonstrated that, the classical MLE 

methods and the various ROS approaches (on raw or log-transformed data) do not perform well in the 

presence of outliers. The estimates obtained using the classical methods in the original scale or in logtransformed scale get distorted by outliers. This results in distorted estimates of intercept (population 

mean) and slope (sd), which gives rise to infeasible extrapolated nondetects. For example, the estimated 

nondetects can become negative (when dealing with raw data), larger than the detection limit (DL), and 

even larger than some of the observed values (e.g., x(k)). The use of such extrapolated NDs results in 

biased estimates of the population mean and sd. Conclusions derived using distorted statistics, UCLs, and 

UPLs can be incorrect and misleading. In these situations, subjective checks may be provided to modify 

the regression method: negative estimates of NDs may be replaced by DL/2, and the estimated nondetects 

greater than DL may be replaced by DL itself. The mean and variance are then computed using these 

replacement values. Singh and Nocerino (2002) considered this method in their simulation study, and 

concluded that the modified regression method also yields biased estimates of population mean and 

variance. 

4.6 Nonparametric Kaplan-Meier (KM) Estimation Method 

The Kaplan-Meier (KM) (1958) estimation method, also known as the product limit estimate (PLE), is 

based upon a statistical distribution function estimate, like the sample distribution function, except that 

this method adjusts for censoring. The KM method is quite popular in survival analysis (dealing with 

right-censored data such as dealing with terminally ill patients) and various other medical applications. 

Some practitioners (e.g., Helsel 2005) are also recommending the use of KM method when dealing with 

left-censored environmental data sets. For data sets with ND observations with multiple detection limits, 

the KM estimation method has been incorporated in ProUCL 4.0. A brief description of the KM method 

to estimate the population mean and standard deviation, and standard error (SE) of the mean for leftcensored data sets is described in this section. For details, refer to Kaplan-Meier (1958) and the report by 

prepared by Bechtel Jacobs Company for DOE (2000). It should be noted that the KM method has an 

added advantage as it can be used on data sets with multiple detection limits. 

Let x1, x2, ..., xn (detection limits or actual measurement) represent n data values obtained from samples 

collected from an area of concern (AOC), and let x1 ′ < x′

2 , ...< x′n denote the n ́ distinct values at which 

detects are observed. That is, n ́ (≤ n) represents distinct observed values in the collected data set of size 

n. For j = 1, ..., n ́ , let mj denote the number of detects at x′

j and let nj denote the number of xi ≤ x′j . 

Also, let x(1) denote the smallest xi. Then 

100 
F ~

(x) = 1, x ≤ xn 

′′

F ~(x) = ∏ n j − mj , x1 ′ ≤ x ≤ xn 

′′

j such that x′

j >x n j 

F ~(x) = F ~(x1 ′) , x(1) ≤ x ≤ x1 ′

F ~(x) = 0 or undefined, 0 ≤ x ≤ x(1) 

Note that in the last equality statement of F ~

(x) above, F ~(x) = 0 when x(1) is a detect, and is undefined 

when x(1) is a nondetect. The estimation of the population mean using the KM method is described as 

follows. 

n′

μˆ = ∑xi ′[F ~(xi ′) − F ~(xi ′

−1 )] , with x0= 0 (4-5) 

i=1 

Using the PLE (or KM) method as described above, an estimate of the standard error (SE) of the mean 

can be obtained. This is given by the following equation. 

n − k 2 n′−1 

2 mi+1 σˆ SE = ∑ai , (4-6)

n − k −1 i=1 ni+1 (ni+1 − mi+1 ) 

where k = number of observation below the detection limit and 

i 

ai = ∑(x′

j+1 − x′j )F ~

(x′

j ) , i: =1, 2, ..., n ́-1. (4-7) 

j=1 

As mentioned before, some researchers (e.g., Helsel 2005) have suggested that this method perhaps is the 

most appropriate to compute the sample mean, SE, and a UCL95 for left-censored data sets. Helsel (2005) 

also suggested using the percentile bootstrap method on the KM estimate of the mean to compute a 95% 

UCL of the population mean. 

Using the KM estimates of the mean and the SE of the mean, some investigators have also suggested 

using a normal distribution-based cut-off value (Helsel, 2005) or a Student’s t-distribution-based cut-off 

value (Bechtel, 2002) to compute a 95% UCL of the mean. Specifically, using a t cut-off value, a 95% 

UCL of the mean based upon the KM estimates is given by the following equation. 

2

σˆ SE UCL95= μˆ + t (4-8) 0.95,(n−1) 

ProUCL 4.0 computes a 95% UCL of the mean based upon the PLE method (KM method) using: 1) the 

normal approximation based upon standard normal critical values, zα; 2) several of the bootstrap methods, 

including the percentile bootstrap method and the bias-corrected accelerated (BCA) bootstrap method, 

and 3) the Chebyshev inequality. It is noted that the approximate KM-UCL95 based upon the normal 

101 
approximation does not provide adequate coverage to the mean of non-normal skewed populations. For 

details of the findings and results of the simulation experiments, refer to Singh, Maichle, and Lee (2006). 

4.7 Bootstrap UCL Computation Methods for Left-Censored Data Sets 

The use of bootstrap methods has become popular with easy access to fast personal computers. For fulluncensored data sets, repeated samples of size n are drawn with replacement (that is each xi has the same 

probability = 1/n of being selected in each of the N bootstrap replications) from the given set of 

observations (as described in Chapter 2). The process is repeated a large number of times, N (e.g., 1000­

2000), and each time an estimate, θ

ˆ of θ (the mean, here) is computed. The estimates thus obtained are 

used to compute an estimate of the standard error of the estimate, θ

ˆ . Just as for the full-uncensored data 

sets without any NDs, for censored data sets also, the bootstrap resamples are obtained with replacement. 

However, an indicator variable, I (0 = detected value, and 1 = nondetected value), is tagged to each 

observation in a bootstrap sample. This process is described as follows. 

Singh, Maichle, and Lee (EPA, 2006) studied the performances (in terms of coverage probabilities) of 

four bootstrap methods (e.g., see Efron and Tibshirani 1993) to compute UCL95 for data sets with BDL 

observations. The four bootstrap methods include standard bootstrap method, bootstrap t method, 

percentile bootstrap method, and the bias-corrected accelerated (BCA) bootstrap method (Efron and 

Tibshirani, 1993, and Many, 1997). These methods are specifically useful when the exact distributions of 

the statistics used or needed (e.g., Cohen’s MLE, RMLE) are not known; or the critical values are not 

available; the test statistics and their distribution depend upon the unknown number of nondetects, k that 

might be present in a data set. For left-censored data sets, the exact (and even approximate) distribution of 

the test statistics and the associated critical values needed to compute the various upper limits (UCL, 

UPL, and UTL) are not known (Kroll, 1996). Some authors (Helsel, 2005) have suggested the use of 

bootstrap methods on full data sets obtained using extrapolated NDs based upon ROS methods, especially 

the robust ROS method. One can also use bootstrap resampling methods directly on left-censored data 

sets. Some bootstrap methods (as incorporated in ProUCL 4.0) to compute upper limits based upon leftcensored data sets are briefly discussed in this section. The details can be found in Singh, Maichle, and 

Lee (EPA, 2006). 

4.7.1 Bootstrapping Data Sets with Nondetect Observations 

Formally, let xnd1, xnd2, ..., xndk, xk+1, xk+2, ..., xn be a random sample of size n from a population (e.g., 

AOC, or background area) with an unknown parameter θ such as the mean, μ, or the pth upper percentile, 

xp, that needs to be estimated (e.g., by the corresponding sample percentile, or by a (1 – α)*100% UCL 

for the pth upper percentile = UTL) based upon the sampled data set with ND observations. As before, the 

sample is left-censored with k nondetect observations, and (n – k) detected data values. Let θ

ˆ be an 

estimate ofθ, which is a function of k nondetect and (n – k) detected observations. For example, the 

parameter, θ, could be the population mean, μ, and a reasonable choice for the estimate,θ

ˆ , might be the 

MLE, robust ROS, gamma ROS, or KM estimate (as discussed earlier) of the population mean. If the 

parameter, θ , represents the p

th upper percentile, then the estimate,θ

ˆ , may represent the p

th sample 

percentile, xˆ p , based upon a full data set obtained using one of the ROS method described above. 

The bootstrap procedure on a censored data set is similar to the general bootstrap technique used on 

uncensored data sets. The only difference is that an indicator variable, I (taking only two values: 0 and 1), 

is assigned to each observation (detected or nondetected) when dealing with left-censored data sets (e.g., 

102 
see Efron, 1981 and Barber and Jennison, 1999). The indicator variables, Ij : j:=1,2,...,n is associated with 

the detection status of the sampled observations, xj ; j: = 1, 2,..., n. Just like simple bootstrap samples, a 

large number, N (e.g., 1000, 2000) of two-dimensional bootstrap resamples, (xiJ, IiJ ),j:= j: = 1, 2,..., N, 

and i: = 1, 2,..., n, of size n are drawn with replacement. The indicator variable, I, takes on a value = 1 

when a detected value is selected and I = 0 if a nondetected value is selected in a bootstrap re-sample. The 

two-dimensional bootstrap process keeps track of the detection status of each observation in a bootstrap 

re-sample. In this setting, the detection limits are fixed, and the number of nondetects may vary from 

bootstrap sample to bootstrap sample. There may be k1 nondetects in the first bootstrap sample, k2 

nondetects in the second sample, ..., and kN nondetects in the Nth bootstrap sample. Since the sampling is 

conducted with replacement, the number of nondetects, ki, i: = 1, 2, ..., N, can take any value from 0 to n 

inclusive. This is typical of a Type I left-censoring bootstrap process. On each of the N bootstrap 

resample, one can use any of the nondetect estimation methods (e.g., KM, MLE, ROS) to compute the 

statistics of interest (e.g., mean, sd, upper limits) using the methods as described in Singh, Maichle, and 

Lee (EPA, 2006). 

It should be noted there is a positive chance that all (or most) observations in a bootstrap resample are 

equal. This is specifically true, when one is dealing with small data sets. In order to avoid such situations 

(with all values in a bootstrap sample to be the same), it desirable to have at least 10-15 (preferably more) 

detected observations in a left-censored data set. It should also be pointed out that it is not advisable and 

desirable to compute statistics based upon a bootstrap resample consisting of only a few detected values 

such as < 4-5. Therefore, it is suggested that all bootstrap resamples consisting of fewer than 4-5 detected 

values should not be used in the computation of the various statistics of interest (e.g., summary statistics, 

upper limits). Bootstrap procedures as incorporated in ProUCL 4.0 use this convention; that is bootstrap 

resamples with less than 4-5 detected values have not been included (in such cases additional resamples 

are drawn) in the estimation process. 

Letθ

ˆ be an estimate of θ based upon the original left-censored data set of size n. For an example, if the 

parameter, θ, represents the population mean, then a reasonable choice for the estimate, θ

ˆ , can be 

Cohen’s MLE mean, ROS mean, or Kaplan-Meier (KM) mean. Similarly, calculate the standard deviation 

(sd) using one of these methods for left-censored data sets. The following two steps are common to all of 

the bootstrap methods incorporated in ProUCL 4.0 

Step 1. Let (xi1, xi2, ... , xin) represent the i

th bootstrap resample of size n with replacement from 

the original left-censored data set (x1, x2, ..., xn). Note that an indicator variable (as 

mentioned above) is tagged along with each data value, taking values 1 (if a detected 

value is chosen) and 0 (if a nondetect is chosen in the resample). Compute an estimate of 

the mean (e.g., MLE, KM, and ROS) using the i

th bootstrap resample, i: = 1, 2, ..., N. 

Step 2. Repeat Step 1 independently N times (e.g., N = 2000), each time calculating new 

estimates (e.g., KM estimates) of population mean. Denote these estimates (e.g., MLE, 

KM means, and ROS means) by x1 , x2 , ..., xN . The bootstrap estimate of the population 

mean is given by the arithmetic mean, xB , of the N estimates xi (N MLEs or N KM 

means). The bootstrap estimate of the standard error is given by: 

1 N 

σˆ B = ∑(xi − xB )

2 . (4-9) N −1 i=1 

103 
In general, a bootstrap estimate of θ may be denoted by θB (instead of xB ). The estimate, θ B is the 

arithmetic mean of the N bootstrap estimates (e.g., KM mean, or MLE mean) given byθ

$

i , i:=1,2,...N. It is 

pointed out that the N bootstrap estimates are computed in the similar way as the original estimate, θ

ˆ 

(e.g., KM or MLE mean) of the parameter, θ. Note that if the estimate, θ

ˆ represents the KM estimate of, 

θ, then θ

$

i (denoted by xi in the above paragraph) also represents the KM mean based upon the ith 

bootstrap resample. The difference, θB − θ

ˆ , provides an estimate of the bias of the estimate, θ

ˆ . After 

these two steps, a bootstrap procedure (BCA, bootstrap t) is similar to a conventional bootstrap procedure 

used on a full data set as described earlier in Chapter 2, and in ProUCL 3.0 Technical Guide (2004). 

A brief description of the various bootstrap UCL computation methods for left-censored data sets is given 

in the following subsections. 

4.7.1.1 UCL of Mean Based Upon Standard Bootstrap Method 

Once the desired number of bootstrap samples, have been obtained following the two steps described 

above, a UCL of mean based upon the standard bootstrap method can be computed as follows. The 

standard bootstrap confidence interval is derived from the following pivotal quantity, t: 

θ

ˆ −θ

t = . (4-10)

σˆ B 

A (1 – α)100% standard bootstrap UCL for θ is given as follows: 

UCL = θ

ˆ + zασˆ B (4-11) 

Here zα is the upper αth critical value (quantile) of the standard normal distribution (SND). It is observed 

that the standard bootstrap method does not adequately adjust for skewness, and the UCL given by the 

above equation fails to provide the specified (1–α)100% coverage to the population mean of skewed (e.g., 

lognormal and gamma) data distributions. 

4.7.1.2 UCL of Mean Based Upon Bootstrap t Method 

This main process is similar to the bootstrap t method as described in Chapter 2 for full-uncensored data 

sets without NDs. A (1–α)100% UCL of the mean based upon the bootstrap t method is given as follows. 

s

 UCL = x − t(αN ) 

x (4-12) 

n 

It should be noted that the mean and sd used in the above equation represent estimates (e.g., KM 

estimates, MLE estimates) obtained using data set with ND observations. Similarly, the t-cutoff value 

used in the above equation is used using the pivotal t-values based upon KM estimates or some other 

estimates obtained using data sets with NDs. Typically, for skewed data sets (e.g., gamma, lognormal), 

the 95% UCL based upon the bootstrap t method performs better than the 95% UCLs based upon the 

simple percentile and the BCA percentile methods. However, it should be pointed out that the bootstrap t 

104 
method sometimes results in unstable and erratic UCL values, especially in the presence of outliers (Efron 

and Tibshirani (1993)) or when the data set may appear to look skewed (perhaps due to the presence of 

contaminated observations). Therefore, the bootstrap t method should be used with caution. In case this 

method results in erratic unstable UCL values, the use of an appropriate Chebyshev inequality-based UCL 

is recommended. Additional suggestions on this topic are described in Chapter 2. 

4.7.1.3 Percentile Bootstrap Method 

In Chapter 2, one can find the description of the percentile bootstrap method. For left-censored data sets, 

sample means are computed for each bootstrap samples using a specified method (e.g., MLE, KM, ROS), 

which are arranged in ascending order. The 95% UCL of the mean is the 95th percentile and is given by: 

95% Percentile – UCL = 95th% xi ; i: = 1, 2, ..., N (4-13) 

For example, when N = 1000, a simple 95% percentile-UCL is given by the 950th ordered mean value 

given by x(950) . It is observed that for skewed (lognormal and gamma) data sets, the BCA bootstrap 

method performs (described below) slightly better (in terms of coverage probability) than the simple 

percentile method. 

4.7.1.4 Bias-Corrected Accelerated (BCA) Percentile Bootstrap Procedure 

It is observed (Singh, Maichle, and Lee, 2006) that for skewed data sets, the BCA method does represent 

a slight improvement (in terms of coverage probability) over the simple percentile method. However, for 

moderately skewed to highly skewed data sets with the sd of log-transformed data >1, this improvement 

is not adequate enough and yields UCLs with a coverage probability lower than the specified coverage of 

0.95. The BCA upper confidence limit of the intended (1-α) coverage for a selected estimation method 

(e.g., MLE, KM, Chebyshev, and so on) is given by the following equation: 

(1- α)100% UCLPROC = BCA – UCL= α 2 x (4-14) PROC

Here α 2 is the α2100th 

x percentile of the distribution of statistics given by ; i: = 1, 2, ..., N, and PROC 

xPROC 

PROC is one of the many (e.g., MLE, KM, DL/2, Chebyshev, and so on) mean estimation methods 

included in this simulation study. 

Here α2 is given by the following probability statement: 

⎡ zˆ + z(1−α ) ⎤

α 2 = Φ

⎣

⎢zˆ0 +

1−αˆ

0

(zˆ0 + z(1−α ))⎦

⎥ (4-15) 

Φ(Z) is the standard normal cumulative distribution function and z(1 – α)

 is the 100(1–α)

th percentile of a 

standard normal distribution. Also, zˆ0 (bias correction) and αˆ (acceleration factor) are given as follows. 

zˆ0 = Φ−1 ⎢

⎡#(xPROC,i < xPROC )

⎥

⎤

, i: = 1, 2, ..., N (4-16)

⎣ N ⎦

105 
Φ-1 (x) is the inverse function of a standard normal cumulative distribution function, e.g., Φ-1 (0.95) = 

1.645 and # αˆ is the acceleration factor and is given by the following equation. 

∑(xPROC − x−i,PROC )

3 

αˆ = (4-17) 6[∑(xPROC − x−i,PROC )

2 ]1.5 

Here the summation in the above equations is being carried from i = 1 to i = n, the sample size. xPROC 

and x−i,PROC are respectively the PROC mean (e.g., KM mean) based upon all n observations, and PROC 

mean of (n-1) observations without the i

th observation, i: = 1, 2, ..., n. 

4.8 Additional UCL Computation Methods for Left-Censored Data Sets 

Some other UCL computation methods as incorporated in ProUCL 4.0 are briefly described in this 

section. The details can be found in Singh, Maichle, and Lee (2006). 

4.8.1 Ad hoc UCL95 Computation Method Based Upon Student’s t-distribution 

Several authors (e.g., Helsel 2005 and Millard 2000) and documents (e.g., USEPA-UGD, 2004) suggest 

the use of Student’s t-statistic to compute a UCL95 for left-censored data sets. That is, using an 

appropriate method (e.g., Cohen’s MLE (CMLE), unbiased MLE (UMLE)) to estimate the mean and sd, 

it has been suggested to use Student’s t-statistic to compute a UCL95. One such UCL95 based upon 

Cohen’s MLE method is given as follows: 

(σˆ 2 UCL95 = μˆ / n) (4-18) MLE + t0.95,(n−1) MLE 

Similar UCL95 equations have been suggested for the RMLE, UMLE, and EM methods. It is noticed that 

for normally distributed left-censored data sets (with low censoring intensities, such as lower than 20%), 

the UCL95 based upon CMLE(t) (or MLE (t)) ad hoc method does provide about 95% coverage to the 

population mean. The coverage by this method decreases slowly as the censoring intensity (percentage of 

NDs) increases. 

4.8.2 (1 – α)100% UCL Based Upon Chebyshev Theorem Using Sample Mean and Sd 

The Chebyshev-Type inequality (as used in Chapter 2) can also be used to compute a UCL95 of mean for 

left-censored data sets. The two-sided Chebyshev theorem (Dudewicz and Misra (1988)) states that given 

a random variable, X, with finite mean and standard deviation, μ1 and σ1, we have 

P(−kσ 1 ≤ X − μ1 ≤ kσ 1 ) ≥ 1−1/ k 2 . 

This inequality can be used to compute a UCL of mean based upon data sets with nondetect observations. 

A (1 – α)100% UCL of population mean, μ1, can be obtained by: 

UCL = x + ((1/ α) −1)s x n . (4-20) 

In the above UCL equation, the sample mean and standard deviation are computed using one of the many 

estimation methods for data sets with ND observations. To obtain such UCLs, instead of using the sample 

106 
mean and sd (or SE of mean), one uses the sample mean and sd (or SE) obtained using the various MLE, 

EM, and KM methods for left-censored data sets as described above. It is noted that the UCL95 based 

upon Chebyshev inequality (with KM estimates) yields a reasonable but conservative UCL of the mean. 

This is especially true for highly skewed data sets from lognormal and gamma distributions. 

4.8.3 UCL95 Based Upon Tiku’s Method (for symmetrical censoring) 

For symmetrical Type II censoring, Tiku (1971) suggested the use of a t-distribution with (n - k-1) 

degrees of freedom. This method can be used on any of the MLE methods (e.g., MLE, RMLE, and 

UMLE). In practice, this method is also used for Type 1 censoring. This is just an approximation method. 

Due to symmetrical censoring, MLE estimates of the mean and standard deviation are independent 

(Schneider 1986), and Student’s t statistic may be used to construct a UCL of the mean. 

A (1 – α)100% UCL of the mean, as proposed by Tiku (1971), is given as follows: 

UCLTk = μˆ MLE + tα,(n−k −1) V (μˆ MLE ) (4-21) 

This can also be written as follows: 

UCLTk = μˆ (n + k +1) (4-22) MLE + tα,(n−k −1) σˆ MLE Gam11 

Here Gam11 is computed using the Fisher’s information matrix (I), (e.g., see Schneider, 1986). Tiku’s 

approximate method is simple and performs better (in terms of coverage probabilities) than Schneider’s 

approximation (Singh, Maichle, and Lee, 2006). 

4.9 Comments on the Use of Substitution Methods and Minimum Sample 

Size Requirements 

It is noted that even though, most of the statistics used to estimate the EPC terms and BTV estimates 

could be computed based upon data sets even with 3-4 detected values; the use of those statistics is not 

recommended due to high level of uncertainty associated with them. It is re-emphasized that when only a 

few (e.g., <4-6) observations are detected, it is not appropriate (due to high uncertainty associated with 

such estimates) to use substitution methods (e.g., DL/2 method), and replace the rest (majority) of the 

nondetect data by their respective DL/2 values (or some other proxy value), and compute an estimate of 

the EPC term or BTV based upon the resulting data set (Singh, Maichle, and Lee (2006)). 

In case, only a small number (e.g., < 4-6) of detected observations are available, it is desirable to collect 

more samples and data (from the area under investigation), so that enough detected observations will be 

available to compute reasonably reliable estimates the various environmental parameters including BTVs 

and EPC terms. If it is not possible to collect more samples (as often is the case), it is suggested to use 

professional judgment and available historical information (e.g., from similar sites) to estimate the BTVs 

rather than using statistical methods on the fabricated data sets obtained using some substitution method 

(e.g., DL/2). In such cases, BTVs and EPC terms may be estimated on case-by-case basis perhaps based 

upon the site knowledge and experts’ opinion. 

107 
4.9.1 Use of Ad hoc Estimation Methods on Case-by-Case Basis 

• For data sets, with high percentage on nondetects (e.g., > 80%, > 90%, ...), the EPC term 

or BTV may be estimated using simple ad hoc methods rather than estimating them based 

upon mean and sd statistics obtained using the fabricated (e.g., 0, DL/2, DL) values. For 

an example, when all or most of the background data values are reported as nondetects, 

the BTV or a not-to-exceed value should also be reported as a nondetect value (or some 

pre-specified action level), perhaps by the maximum RL or maximum RL/2. 

• The median or mode (instead of using mean and sd based upon fabricated data) of the 

data with majority of the ND values may also be used to estimate EPC terms or BTVs. 

• Also, when only a few detected values (< 4-6) are available in a data set of larger size 

(e.g., > 15-20), one may again use the maximum RL, or maximum RL/2 as an estimate of 

the BTV or some pre-specified action level. 

• When only a few detected values (e.g., < 4-6) are available in data sets of smaller sizes 

(e.g., < 8-10), one may use the maximum detected data value or the second largest 

detected value to estimate the BTV. 

• The uncertainty associated with all such estimates listed above will be high; and 

statistical properties such as bias, accuracy, and precision of such estimates would remain 

unknown. 

• Statistics is the science of estimating population values (e.g., EPC, BTV) based upon 

sampled data values representing a much smaller but representative fraction of the 

population under study. In order to be able to use defensible statistical methods and 

computing reliable estimates, it is desirable to have adequate amount of data. 

Specifically, when a data set associated with a COPC consists of at least 8-10 detected 

(more are desirable) observations, one can use one of the recommended methods as 

incorporated in ProUCL 4.0. The details of those methods are given in Singh, Maichle, 

and Lee (EPA, 2006). 

• Thus, it is recommended to avoid using statistical methods (to estimate the BTVs and 

other environmental parameters) on data sets with less than 4-5 detected values (more are 

desirable). If possible, it is desirable to collect more samples (data) with detected values. 

Statistics computed based upon small data sets or on data sets with only a few detected 

values (e.g., < 4-6) cannot be considered reliable enough to make important remediation 

and cleanup decisions potentially affecting the human health and the environment. 

4.10 Summary and Recommendations 

The following observations and recommendations as incorporated in ProUCL 4.0 are based upon the 

results and findings of the study and simulation experiments as summarized in Singh, Maichle, and Lee 

(EPA, 2006). 

108 
4.10.1 General Observations and Comments 

• It is not easy to verify (perform goodness-of-fit) the distribution of a left-censored data 

set. Therefore, emphasis is given on the use of nonparametric upper limit (e.g., UCLs, 

UPLs, and UTLs) computation methods. 

• Most of the parametric MLE methods (e.g., MLE, RMLE, and EM) assume that there is 

only one detection limit. But in practice, a left-censored data set often has multiple 

detection limits. For such methods, the KM method or one of the ROS methods as 

incorporated in ProUCL 4.0 may be used. 

• For reliable and accurate results, the user should ensure that the data set under study 

represents a single statistical population (e.g., background reference area, or an AOC) and 

not a mixture population (e.g., clean and polluted site areas). 

• It is recommended to identify all potential outliers and investigate them separately. 

Decisions about the disposition of outliers should be made by all interested members of 

the project team. Several references are available in the literature to properly identify 

outliers (Rousseeuw and Leroy (1987) and Singh and Nocerino (1995)), and to partition a 

mixture sample into component sub-samples (Singh, Singh, and Flatman (1994)). A new 

chapter describing the population partitioning methods has been included in the revised 

Guidance for Comparing Background and Chemical Concentrations in Soil for CERCLA 

Sites (EPA, 2002b), currently under revision by the NERL, Technical Support Center, 

EPA, Las Vegas. 

• Avoid the use of transformations (to achieve symmetry) while computing the upper limits 

for various environmental applications. It is easier and safer to interpret the results 

computed in the original scale. Moreover, the results and statistics computed in the 

original scale do not suffer from transformations bias. 

• Specifically, avoid the use of a lognormal model even when the data appear to be 

lognormally distributed. Its use often results in incorrect and unrealistic statistics of no 

practical merit (Singh, Singh, and Iaci, 2002). 

• The parameter in the transformed space may not be of interest to make cleanup decisions. 

The cleanup and remediation decision are often made in original raw scale; therefore, the 

statistics (e.g., UCL95) computed in transformed space need to be back-transformed in 

the original scale. It is not clear to a typical user how to back-transform results in logscale or any other scale obtained using a Box-Cox (BC)-type transformation to original 

raw scale. Moreover, transformed results suffer from significant amount of 

transformation bias. 

• It is recommended to avoid the use of equation (4-2) as described in Shaarawi (1989) to 

back-transform estimates from the log-scale to original scale. The question now arises - 

how one should back-transform results from a log-space (or any other transformed space) 

to the original space. Unfortunately, no defensible guidance is available in the 

environmental literature to address this question. Moreover, the back-transformation 

formula will change from transformation to transformation (BC-type transformations), 

and the bias introduced by such transformations will remain unknown. This is one of the 

109 
main reasons that ProUCL 4.0 does not compute MLE estimates (or other estimates such 

as fully parametric estimates using ROS on logged data) using log-transformed data. 

• Therefore, in cases when a data set in the “raw” scale cannot be modeled by a parametric 

distribution, it is desirable to use nonparametric methods rather than testing or estimating 

some parameter in the transformed space. 

• For the various parametric (gamma and lognormal) and nonparametric skewed 

distributions, the performance (in terms of coverage percentage for the population mean) 

of Robust ROS method followed by percentile or BCA bootstrap methods is not better 

than the KM (Chebyshev) and KM (BCA) UCL methods. It is also observed that, for leftcensor data sets of all sizes and various censoring levels, the robust ROS UCL (both 

percentile bootstrap and BCA bootstrap methods) fail to provide adequate coverage for 

the population mean of highly skewed distributions. 

• On page (78) of Helsel (2005), the use of the robust ROS MLE method (Kroll, C.N. and 

J.R. Stedinger, 1996) has been suggested to compute summary statistics. In this hybrid 

method, MLEs are computed using log-transformed data. Using the regression model as 

given by equation (4-1), the MLEs of mean (used as intercept) and sd (used as slope) in 

the log-scale are used to extrapolate the NDs in the log-scale. Just like in Helsel’s robust 

ROS method, all of the NDs are transformed back in the original scale by exponentiation. 

This results in a full data set in the original scale. One may then compute the mean and sd 

using the full data set. The estimates thus obtained are called robust ROS ML estimates 

(Helsel (2005), and Kroll and Stedinger, 1996). However, the performance of such a 

hybrid estimation method is not well known. Moreover, for higher censoring levels, the 

MLE methods sometimes behave in an unstable manner. 

o It should be noted that the performance of this hybrid method is unknown. 

o It is not known why this method is called a robust method. 

o The stability of the MLEs obtained using the log-transformed data is doubtful 

especially for higher censoring levels. 

o The BCA and (%) UCLs based upon this method will fail to provide the adequate 

coverage for the population mean for moderately skewed to highly skewed data sets. 

• The maximum censoring level considered in the present simulation study is 70%. For 

data sets having a larger % of nondetects (e.g., 80%, 90%, or 99% nondetects), statistical 

estimates may not be reliable. Decisions about the use of an appropriate method should 

be made by the risk assessors and regulatory personnel on a site-specific basis. The use of 

nonparametric methods based upon the hypothesis testing for proportion of exceedances 

is recommended in such cases (USEPA (2006); Helsel (2005)) with % censoring 

exceeding 70%-80%. This method, the single sample hypothesis testing for proportions, 

is also available in ProUCL 4.0. 

• Do Not Use DL/2 (t) UCL method. This UCL computation method does not provide 

adequate coverage (for any distribution and sample size) for the population mean, even 

for censoring levels as low as 10%, 15%. This is contrary to the conjecture and assertion 

(e.g., EPA (2006)) often made that the DL/2 method can be used for lower (≤ 20%) 

censoring levels. The coverage provided by the DL/2 (t) method deteriorates fast as the 

110 
censoring intensity increases. The DL/2 (t) method is not recommended by the authors or 

developers of this text and ProUCL 4.0. 

• The KM method is a preferred method as it can handle multiple detection limits. 

Moreover, the various nonparametric UCL95 methods (KM (BCA), KM (z), KM (%), 

KM (t)) based upon the KM estimates provide good coverages for the population mean. 

• For a symmetric distribution (approximate normality), several UCL95 methods provide 

good coverage (~95%) for the population mean, including the Winsorization mean, 

Cohen’s MLE (t), Cohen’s MLE (Tiku), KM (z), KM (t), KM (%) and KM (BCA). 

Specific recommendations for the various distributions considered in this report are 

described as follows. 

4.10.2 Recommended UCL95 Methods for Normal (Approximate Normal) Distribution 

• For normal and approximately normal (e.g., symmetric) distributions, the most 

appropriate UCL95 computation methods are the KM (t) or KM (%) methods. For 

symmetric distributions, both of these methods perform equally well on left-censored 

data sets for all censoring levels and sample sizes. 

4.10.3 Recommended UCL95 Methods for Gamma Distribution 

• For highly skewed gamma distributions, G(k, θ), with a shape parameter, k ≤ 1: 

o Use the nonparametric KM (Chebyshev) UCL95 method for censoring levels < 30%, 

o Use the nonparametric KM (BCA) UCL95 method for censoring levels in the interval 

[30%, 50%), 

o Use the nonparametric KM (t) UCL95 method for censoring levels ≥ 50%. 

• For moderately skewed gamma distributions, G (k, θ), with shape parameter, 1< k ≤ 2: 

o For censoring level ≤ 10%, use the KM (Chebyshev) UCL95 method, 

o For higher censoring levels (10%, 25%, ...), use the KM (BCA) UCL95 method, 

o For censoring levels in [25%, 40%), use the KM (%) UCL95 method, 

o For censoring levels ≥ 40%, use the KM (t) UCL95 method. 

• For mildly skewed gamma distributions, G (k, θ), with k > 2: 

o Use the KM (BCA) UCL95 method for lower censoring levels (≤ 20%) 

o For censoring levels in the interval (20%, 40%), use the KM (%) UCL95, 

o For censoring ≥ 40%, use the KM (t) UCL95 computation method. 

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4.10.4 Recommended UCL95 Methods for Lognormal Distribution 

• For mildly skewed data sets with σˆ ≤ 1: 

o For censoring levels (≤ 20%) and sample of sizes less than 50-70, use the KM 

(Chebyshev) UCL95, 

o For censoring levels (≤ 20%) and samples of sizes greater than 50-70, use the KM 

(BCA) UCL95, 

o For censoring levels in the interval (20%, 40%) and all sample sizes, use the KM 

(BCA) UCL95, 

o For censoring level ≥ 40%, use the KM (%) or KM (t) UCL95 method. 

• For data sets with σˆ in the interval (1, 1.5]: 

o For censoring levels ≤ 50% and samples of sizes < 40, use 97.5% the KM 

(Chebyshev) UCL, 

o For censoring levels ≤ 50%, samples of size ≥ 40, use 95% KM (Chebyshev) UCL, 

o For censoring levels > 50%, use the KM (BCA) UCL95 for samples of all sizes. 

• For highly skewed data sets with σˆ in the interval (1.5, 2]: 

o For sample sizes < 40, and censoring levels <50%, use 99% KM (Chebyshev) UCL, 

o For sample sizes ≥ 40 and censoring levels < 50%, use 97.5% KM (Chebyshev) 

UCL, 

o For samples of sizes < 40-50 and censoring levels ≥ 50%, use the 97.5% KM 

(Chebyshev) UCL, 

o For samples of sizes ≥ 40-50, censoring ≥ 50%, use 95% KM (Chebyshev) UCL. 

• Use a similar pattern for more highly skewed data sets with σˆ > 2.0, 3.0: 

o For extremely highly skewed data sets, an appropriate estimate of the EPC term (in 

terms of adequate coverage) is given by a UCL based upon Chebyshev inequality and 

KM estimates. The confidence coefficient to be used depends upon the skewness. For 

highly skewed data sets, a higher (e.g., > 95%) confidence coefficient may have to be 

used to estimate the EPC. 

o As the skewness increases, the confidence coefficient also increases. 

o For such highly skewed distributions (with σˆ > 2.0, 3.0), for lower sample sizes (e.g., 

< 50-60), one may simply use a 99% KM (Chebyshev) UCL to estimate the 

112 
population mean, EPC term, and other relevant threshold values. For sample sizes 

greater than 60, one may use a 97.5% KM (Chebyshev) UCL as an estimate of the 

population mean or mass. 

o For sample sizes greater than 60, one may use a 97.5% KM (Chebyshev) UCL as an 

estimate of the population mean or mass. 

4.10.5 Recommended UCL95 Methods for Non-Discernable Distributions 

• For symmetric or approximately symmetric nonparametric data distributions, one may 

use the same UCL computation methods as for the data sets coming from a normal or an 

approximate normal or symmetric population. 

• It is noted that most of the recommended UCL computation methods for a lognormal 

distribution do not assume the lognormality of the data set. Therefore, those UCL 

computation methods can be used on skewed nonparametric data sets that do not follow 

any of the well-known parametric distributions. 

113 
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Chapter 5 

Estimating Background Threshold Values and Establishing SiteSpecific Background Concentrations Using Data Sets with 

Nondetect (ND) Observations 

5.1 Introduction 

As discussed in Chapter 4, nondetects (NDs) or below detection limit (BDL) observations are inevitable 

in most environmental data sets. Environmental scientists often encounter trace level concentrations of 

contaminants of potential concern (COPC) when evaluating sampled analytical results. Those low level 

analytical results cannot be measured accurately, and therefore are typically reported as less than one or 

more detection limit (DL) values. However, the practitioners (e.g., environmental scientists) do need to 

obtain reliable estimates (point estimates and interval estimates) of the population parameters such as the 

population mean and population percentiles. Parametric and nonparametric methods to compute interval 

estimates of population mean (e.g., UCLs) for data sets with NDs are described in Chapter 4. Specifically, 

Chapter 4 of this Technical Guide discusses in detail the various GOF tests, methods to estimate 

population mean and sd, and parametric and nonparametric UCL computation methods based upon data 

sets with nondetect observations. All of those GOF tests and methods to estimate the population mean and 

sd as described in Chapter 4 are also applicable to this Chapter 5. Chapter 3 of the Technical Guide 

discusses various parametric and nonparametric methods to compute upper limits (UPLs, UTLs, and 

upper percentiles) that can be used to estimate the background threshold values (BTVs) and other not-toexceed values. This chapter discusses parametric and nonparametric methods as incorporated in ProUCL 

4.0 that can be used to compute upper limits including UPLs, UTLs, and upper percentiles often used to 

estimate the BTVs based upon data sets with ND observations. 

Even though several methods are available (as described in Chapter 4) to estimate population mean and sd 

based upon data sets with nondetects, it is noted that not much guidance is available in the statistical 

literature on how to compute appropriate UPLs and UTLs based upon left-censored data sets of varying 

degree of skewness. The estimation methods (e.g., MLE, ROS, KM, bootstrap) to estimate the population 

mean and standard deviation as incorporated in ProUCL 4.0 are described in Chapter 4. Using the results 

and findings of the study performed by Singh, Maichle, and Lee (2006), both parametric and distributionfree (nonparametric) methods and limits used to estimate the BTVs and not-to-exceed values (as 

incorporated in ProUCL 4.0) based upon left-censored data sets are briefly described in this chapter. More 

details of the computations of upper limits can be found in the newly added Chapter 6 of the revised 

Guidance for Comparing Background and Chemical Concentrations in Soil for CERCLA Sites (EPA, 

2002b). It is noted that ProUCL 4.0 is perhaps the first software package, which has several statistical 

methods including the KM and bootstrap methods that can be used to estimate the BTVs and not-toexceed values based upon left-censored data sets with ND observations. 

As mentioned in earlier chapters, it is not easy to reliably perform GOF tests on left-censored data sets, 

especially when the percentage of nondetects is quite high (e.g., > 40%) and the number of detected 

observations is small (e.g., < 8-10). Therefore, just as in Chapter 4, in this chapter also emphasis is given 

to the computation of upper limits (UPLs, UTLs, upper percentiles) based upon distribution-free 

nonparametric methods such as the Kaplan-Meier (KM) method, Chebyshev inequality, and other 

computer intensive bootstrap resampling methods. It should be noted that, in practice the Chebyshev 

inequality often yields conservative estimates (providing higher coverage than the specified 90% or 95% 

coverage) of the BTVs and other not-to-exceed values. Therefore, instead of using a 95% UPL based 

115 
upon Chebyshev inequality, it is suggested to use Chebyshev UPL with a lower confidence coefficient 

(e.g., 85%, 90%) as an estimate of BTV and not-to-exceed value. 

Note: Unless, the data are highly skewed, it is preferred to use KM estimates and the associated upper 

limits to estimate the BTVs and not-to-exceed values. Just as in Chapter 3, no specific recommendations 

have been made regarding the most appropriate upper limit(s) that may be used to estimate the BTVs and 

not-to-exceed values. However, the author of this chapter prefers to use UPLs as estimates BTVs and 

other not-to-exceed values. 

Once again, throughout this chapter, it is assumed that the user is dealing with a data set collected from a 

single background population; has pre-processed the data set, and has identified all of the potential 

outliers (if any) and multiple populations. In order to obtain meaningful, reliable, and practical results, the 

procedures described in this chapter should be used on data sets that represent “single” (e.g., a 

background area), and “not mixture” populations. These statements are made to familiarize the user with 

the underlying assumptions required by the various statistical methods, including the estimation methods 

based upon left-censored data sets. Some simple classical outlier tests as incorporated in ProUCL 4.0 are 

discussed in Chapter 7 of this Technical guidance document. 

Note: It should be noted that the mathematical UPL and UTL computation formulae and methods as 

described in this chapter (and incorporated in ProUCL 4.0) could be used on any left-censored data set 

with or without the outliers. The user should keep this in mind that the estimates of the BTVs based upon 

data sets with potential outliers or mixture populations may not be reliable. 

Background evaluation and comparison studies often require the computation of UPLs and UTLs based 

upon left-censored data sets. Recent environmental literature (e.g., Millard (2002) and USEPA-UGD 

(2004)) lists and cites the use of ad hoc “rule-of-thumb” type methods based upon the Student’s t-statistic 

or Land’s H-statistic to compute 95% UCLs, 95% UPLs, and 95% UTLs. For example, it is noted that the 

Student’s t-statistic (e.g., on Cohen’s maximum likelihood estimates) is used to compute an upper 

prediction limit (pages 10-26, USEPA-UGD (2004)). However, it is noted that the distribution of the ttype statistic based upon the MLE or KM estimates of mean and standard deviation used to construct a 

UPL (pages 10-26, USEPA-UGD, 2004) is not known. The MLEs or KM estimates of population mean 

and standard deviation (sd) based upon left-censored data sets are very different from the traditional mean 

and sd used in the definition of a typical Student’s t-statistic. The Student’s t-statistic-based “rule-ofthumb” methods to compute UCLs, UPLs and UTLs are difficult to defend for moderately skewed to 

highly skewed data sets with standard deviation of the log-transformed data exceeding 0.75-1.0. 

5.2 Underlying Assumptions 

Pre-processing of data to identify potential outliers and multiple populations (if any) should be conducted 

to obtain accurate and reliable estimates of the environmental parameters such as BTVs and exposure 

point concentration (EPC) terms. The user may want use informal graphical displays (e.g., quantilequantile plots, histograms) and formal population partitioning methods (e.g., see Singh, Singh, and 

Flatman (1994)) to identify multiple populations (if any). A simple iterative population partitioning 

method is presented in the newly added Chapter 7 of the revised Guidance for Comparing Background 

and Chemical Concentrations in Soil for CERCLA Sites (EPA, 2002b). The estimation and upper limits 

computation methods as described in this chapter should be used on data sets coming from a “single” 

statistical population such as a single contaminated or remediated area of the site, an unimpacted clean 

background or reference population. The sampled data set should represent a random sample from the 

116 
area under study such as a background area (BA). This means that the data set should be representative of 

the entire background population of interest under study. 

A few outlying observations (e.g., representing contaminated locations) in a full-uncensored data set or in 

a left-censored data set often distort all statistics including the maximum likelihood estimates (MLEs) of 

mean and standard deviation (sd); Kaplan-Meier (KM) estimates; and regression on order statistics (ROS) 

estimates (e.g., slope and intercept, and extrapolated NDs) both in raw as well as log-scale (Singh and 

Nocerino, 2002). The use of such distorted estimates of mean, sd, and of ND values (e.g., using ROS) will 

yield distorted estimates (e.g., UPLs, UTLs, upper percentiles) of BTVs and not-to-exceed values. 

Therefore, instead of computing distorted estimates for the entire area of interest (e.g., BA, AOC), it is 

desirable to identify potential outliers and study them separately. 

Identified environmental outliers perhaps represented by their spatial locations, time period, laboratory, or 

analytical methods require further and perhaps separate investigation. Reliable and defensible statistics 

can be obtained based upon the majority of a data set representing the main body of the dominant 

population (e.g., reference area, BA) under study. Instead of computing distorted statistics and estimates 

by including a few low probability outlying observations, it is desirable that those low probability 

extreme high outliers coming from the upper tail of the data distribution should be treated separately. 

Note: The main objective of using a statistical procedure is to model the majority of the data representing 

the main dominant population, and not to accommodate a few low probability outliers that may yield 

inflated and impractical results. The background thresholds should be estimated by reliable statistics 

(and not distorted statistics) obtained using data sets representing the dominant background population. 

High outlying values contaminate the underlying left-censored or uncensored full data set from the 

population under study. In practice, it is the presence of a few extreme outliers that cause the rejection of 

normality of a data set (Barnett and Lewis (1994)). 

As mentioned before, statistics (e.g., mean, UCL, UPL) based upon data sets with outliers would yield 

distorted and inflated estimates for entire population (e.g., mean, upper threshold value, upper limits), 

which in turn may result in incorrect conclusions and decisions. In such cases, it is suggested to compute 

all relevant statistics using data sets with outliers and without outliers, and compare the results. This 

extra step often helps to see the direct potential influence of outlier(s) on the various statistics of interest 

(e.g., mean, UPLs). This in turn will help the project team to make informative decisions about the 

disposition of outliers. That is, the project team and experts familiar with the site should decide which of 

the computed statistics (with outliers or without outliers) represent better and more accurate estimate(s) 

of the population parameters (e.g., mean, EPC, BTV) under consideration. Since the treatment and 

handling of outliers is a controversial and subjective topic, it is suggested that the outliers be treated on a 

site-specific basis using all existing knowledge about the site and the site background (e.g., EA, area of 

concern (AOC), reference area) under investigation. 

5.3 Identification of High (in Upper Tail) Outliers for Left-Censored Data Sets 

As well known, outliers when present in uncensored or censored (with NDs) data sets distort all statistics 

of interest. Decisions based upon distorted (inflated) estimates of the BTVs and not-to-exceed values can 

be hazardous to human health and the environment. It is, therefore important that outliers be identified 

before computing the estimates of BTVs and not-to-exceed values. Therefore, the user should make a 

sincere effort to identify all potential outliers using effective, robust and resistant statistical methods (e.g., 

Rousseeuw and Leroy (1987), Barnett and Lewis (1994), Singh (1993), and Singh and Nocerino (1995)) 

before proceeding with the estimation of population mean, standard deviation, UCLs, UPLs, UTLs, and 

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other summary statistics based upon left-censored data sets. Some robust and resistant estimation methods 

for left-censored data sets are given in Singh and Nocerino (1995). The detailed discussion of those robust 

and resistant estimation methods is beyond the scope of ProUCL 4.0. A couple of classical outlier tests 

(e.g., Dixon test and Rosner test) are available in ProUCL 4.0. These tests can be used on data sets with 

and without nondetect observation. 

Additionally, one can always (it is recommended) use graphical displays such as Q-Q plots and box plots 

to visually identify high outliers in a left-censored data set. It should be pointed out that in many 

environmental applications; it is the identification of high outliers (perhaps representing contaminated 

locations and hot spots) that is important. The occurrence of nondetect (less than values) observations and 

other low values is quite common in environmental data sets, especially when the data are collected from 

a background or a reference area. 

5.3.1 Outlier Testing Procedures for Data Sets with NDs 

For the purpose of the identification of high outliers, one may replace the nondetect values by their 

respective detection limits or half of the detection limits, or may just ignore them (especially when the 

number of detected values is large such as exceeding 8-10) from any of the outlier test (e.g., Rosner test, 

Dixon test) computation including the graphical displays such as Q-Q plots. These simple outlier testing 

procedures (ignoring NDs, and replacing them by DL/2) are available in ProUCL for data sets with ND 

values. Except for the identification of high outlying observations, the outlier testing statistics (computed 

with NDs or without NDs) are not used in any of the estimation and decision making process. Therefore, 

for the purpose of testing for high outliers, it does not matter how the nondetect observations are treated. 

After outliers have been determined (this should also be verified using graphical displays), the project 

team and experts familiar with the site should make the final decision about the proper disposition of 

outliers. 

5.3.2 Q-Q Plots for Data Sets with Nondetect Observations 

ProUCL 4.0 can be used to obtain Q-Q plots using only the detected data values, or based upon full data 

sets obtained using some proxy values (e.g., DL, DL/2) for nondetect observations. ProUCL 4.0 also 

generates Q-Q plots based upon full data sets obtained using estimated NDs based upon ROS methods 

(e.g., normal, lognormal, gamma). The details of those ROS methods are described in Chapter 4. Thus 

outlier test statistics (e.g., Rosner test) as discussed in Chapter 7, and the visual displays shown by Q-Q 

plots can identify high outlying observations that may be present in an environmental data set. It is 

suggested to use both graphical displays as well outlier test statistics to test and confirm the presence of 

outlier(s) in an environmental data set. Some examples illustrating the influence of outliers on estimates 

of BTVs are also considered in later sections of this chapter. 

Note: NERL-EPA, Las Vegas is currently upgrading Scout software package (EPA, 1999) that will 

provide state-of-the-art robust and resistant multiple outlier identification procedures for both univariate 

as well as multivariate data sets. 

5.4 Estimating BTVs and Not-to-Exceed Values Based Upon Left-Censored 

Data Sets 

This section describes some parametric and nonparametric methods to compute upper limits (e.g., UPLs, 

UTLs, upper percentiles) that may be used to estimate the BTVs and other not-to-exceed levels based 

upon data sets with nondetect observations. As described in Chapter 3, sample statistics such as upper 

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percentiles, UPLs, and UTLs are often used as estimates of BTVs, compliance limits, or not-to-exceed 

values. It is noted that for left-censored data sets, there are no preferred and recommended methods 

available in the literature that can be used to estimate the BTVs and not-to-exceed values. Some ad hoc 

computation methods based upon the Student’s t-statistic (assuming Student’s t-distribution without 

theoretical justification) or normal Z-scores have been cited or mentioned in the literature (Helsel (2005), 

Millard (2002), USEPA-UGD (2004)) to compute percentiles, UPLs, and UTLs based upon statistics 

(e.g., mean, sd) obtained using MLE method, KM method, or ROS methods (California’s Ocean Plan 

(2005)). 

However, it should be noted that the t-type statistic used to derive such limits (e.g., equation (5-1) given 

below) does not really follow the Student’s t-distribution as assumed in the references listed above. These 

methods to compute upper limits (e.g., UPL, UTL, and percentiles) based upon Student’s t-distribution, 

non-central t-distribution, or normal Z-scores are described in this chapter for historical reasons; and the 

use of such methods in not recommended due to lack of theoretical justification. These methods may 

yield reasonable upper limits (e.g., with proper coverage) for mildly skewed data sets with sd of logtransformed data less than 0.75-1.0. Singh, Maichle, and Lee (EPA 2006) demonstrated that the use of 

Student’s t-statistic on moderately to highly skewed left-censored data sets yields UCL95 with coverage 

probabilities much lower than the specified confidence coefficient, 0.95. Moreover, based upon the results 

and conclusions summarized in Singh, Maichle, and Lee (2006), it is anticipated that the performance of 

Student’s t-type upper limits (e.g., UPLs, UTLs) based upon MLE estimates for moderately skewed to 

highly skewed data sets would be less than acceptable. 

Therefore, instead of making incorrect assumptions or using parametric methods without theoretical 

justification, the use of nonparametric methods is preferred to estimate BTVs. Several of those methods 

including bootstrap methods for left-censored data sets (Chapter 4) are available in ProUCL 4.0. Methods 

as incorporated in ProUCL 4.0 to compute upper limits (UPLs, UTLs, and upper percentiles) based upon 

data sets with ND observations are described in the following sections. 

Note: It should also be noted that the BTV estimation methods for full data sets as described in Chapter 3 

and available in ProUCL 4.0 may also be used on generated data sets obtained using ROS methods on 

data sets with nondetect observations. Moreover, all other comments about the use of substitution 

methods, disposition of outliers, and minimum sample size requirements as described in Chapter 4 also 

apply to BTV estimation methods for data sets with nondetect observations as described in this chapter. 

5.4.1 Computing Upper Prediction Limits (UPLs) for Left-Censored Data Sets 

This section describes some parametric and nonparametric methods to compute UPLs based upon leftcensored data sets. 

5.4.1.1 UPLs Based Upon Student’s t-type Statistic 

As noted before, some documents (e.g., Helsel (2005), Millard (2002), and Unified Guidance Document 

(UGD)-EPA (2004)) list or mention the use of Student’s t-statistic as one of the potential methods to 

compute UCL95, UPL95, and UTLs. Specifically, KM estimates (Helsel (2005)) or Cohen’s MLEs of 

mean and standard deviation are used to compute Student’s t-statistic-based UPL as an estimate of the 

BTV. For an example, a (1 – α)100% UPL for a future (or next, or from a different population) 

observation (observation not belonging to the collected background sample under study) based upon MLE 

estimates is given by the following equation. 

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ˆ ˆ 2 UPL = μ n (5-1) MLE + t((1 −α ),( n−1)) σ MLE (1+1/ ) 

Here t((1−α ),(n−1)) is the critical value of Student’s t-distribution with (n–1) degrees of freedom (df). Similar 

UPL equations for a single future observation and also for the next (or future) k≥1 observations (as 

described in Chapter 3) can be developed for other estimation methods including the KM and ROS 

methods. If the distributions of the site data and the background data are comparable, then a new (next) 

observation coming from the site population (e.g., site) should lie at or below the UPL95 probability 0.95. 

It is noted that, just like UCLs, the UPLs based upon Student’s t-distribution might fail to provide the 

specified (e.g., 0.95) coverage, especially when data are moderately skewed to highly skewed with sd of 

log-transformed data > 1.0. 

5.4.1.2 UPL Based Upon the Chebyshev Inequality 

The Chebyshev inequality can also be used to obtain a reasonably conservative but stable estimate of the 

BTV, and is given as follows. 

UPL = x +[ ((1/ α) −1)*(1 +1/ )] x ns (5-2)

The mean x and standard deviation, sx used in the above equation are computed using one of the 

estimation methods (e.g., KM, MLE, ROS method) on the left-censored data set. Since this Chebyshev 

method does not require any distributional assumptions about the data set under study, this is a 

nonparametric method. It should be noted that just like the Chebyshev UCL, UPL based upon Chebyshev 

inequality often yields much higher estimate of not-to-exceed values and BTVs than various other 

methods as described in this chapter. This is especially true when skewness is mild (e.g., sd of logtransformed data is low < 0.75-1.0), and the sample size is large (e.g., > 30). The user is advised to use 

professional judgment before using this method to compute a UPL. Specifically, instead of using a 95% 

UPL based upon Chebyshev inequality, it is suggested to use Chebyshev UPL with a lower confidence 

coefficient (e.g., 85%, 90%) as an estimate of BTV and not-to-exceed value. ProUCL 4.0 can compute 

these limits for any level of confidence coefficient. 

5.4.1.3 UPLs Based Upon ROS Methods 

As described earlier, ROS methods first predict k nondetect values using an OLS linear regression model 

(Chapter 4). This results in a full data set of size n. For ROS methods (normal, gamma, lognormal), 

ProUCL 4.0 generates additional columns consisting of (n-k) detected values, and k predicted values of 

the k nondetect observations for each variable selected by the user. Once, the nondetect observations have 

been estimated, an experienced user may use any of the available parametric and nonparametric BTV and 

not-to-exceed value estimation methods for full data sets (without NDs) as described in Chapter 3 and 

incorporated in ProUCL 4.0. Those estimation methods are not repeated here. The user of this method is 

assumed to know the behavior of the various ROS methods as incorporated in ProUCL 4.0. Specifically, 

it is expected that the user knows how the presence of outliers can yield distorted and infeasible estimates 

of ND observations. 

Note: It is noted that a linear model (regression line) can be obtained even when only two (2) detected 

observations are available. Therefore, the methods as discussed here and also incorporated in ProUCL 

4.0 can be used on data sets with 2 or more detected observations. Obviously, in order to be able to 

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compute reliable estimates of nondetects and to compute defensible upper limits, enough detected 

observations should be made available. 

5.4.2 Computing Upper p*100% Percentiles for Left-Censored Data Sets 

This section briefly describes some parametric and nonparametric methods to compute upper percentiles 

based upon left-censored data sets. 

5.4.2.1 Upper Percentiles Based Upon Standard Normal Z-Scores 

The use of standard normal Z-scores (e.g., Helsel, 2005) has also been listed as one of the potential 

method to estimate upper percentiles using KM estimates or MLE estimates based upon left-censored data 

sets. The p

th percentile based upon KM estimates (as incorporated in ProUCL 4.0) is given as follows. 

2

xˆ p = μˆKM + z p σˆKM (5-3) 

Here zp is the p*100th percentile of a standard normal, N (0, 1) distribution, which means that the area 

(under the standard normal curve) to the left of zp is p. If the distributions of the site data and the 

background data are comparable and similar (meaning no contaminated observations from the site), then 

an observation coming from a population (e.g., site) similar (comparable) to that of the background 

population should lie at or below the p*100% percentile, with probability p. The 95th normal percentile 

given by the above equation (for p = 0.95) represents one of the many estimates of the BTVs. 

5.4.2.2 Upper Percentiles Based Upon ROS Methods 

As noted in Chapter 4, all ROS methods first predict k nondetect values using an OLS linear regression 

model (Chapter 4) assuming a specified distribution of detected and nondetected observations. This 

process results in a full data set of size n consisting of k extrapolated NDs and (n-k) detected values. For 

ROS methods (normal, gamma, lognormal), ProUCL 4.0 generates additional columns consisting of the 

(n-k) detected values, and k predicted values of the k nondetect observations for each variable selected by 

the user. Once, the nondetect observations have been estimated, an experienced user may use any of the 

parametric or nonparametric percentile computation methods (e.g., gamma percentiles) for full data sets 

as described in Chapter 3 and incorporated in ProUCL 4.0. 

5.4.3 Computing Upper Tolerance Limits (UTLs) for Left-Censored Data Sets 

This section briefly describes some parametric and nonparametric methods to compute UTLs based upon 

left-censored data sets. 

5.4.3.1 UTLs Based Upon K Critical Values Obtained Using a Non-Central t-Distribution 

Just like Student’s t-statistic-based UPL, the use of the following equation has been mentioned to 

compute an upper (1 – α)100% tolerance limit with tolerance or coverage coefficient = p (that is 

providing coverage to at least p*100% of observations): 

2 UTL = μˆMLE + K * σˆMLE (5-4) 

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Here K = K (n, α, p) is the tolerance factor and depends upon the sample size, n, confidence coefficient 

(CC) = (1- α), and the coverage proportion = p. The K critical values are based upon non-central tdistribution, and have been tabulated extensively in the statistical literature (e.g., Hahn and Meeker 

(1991)). It should be pointed out that for left-censored data sets, the distribution of the MLEs (or KM 

estimates) based-statistic used to develop a UTL is unknown, and hence the use of non-central tdistribution-based K-cut off values has no theoretical backup and justification. The use of similar UTLs 

based upon other estimation methods including the KM and ROS methods has also been suggested. These 

UTLs are described here for historical reasons. The use of such UTLs without theoretical justification is 

not recommended. 

5.4.3.2 UTLs Based Upon ROS Methods 

The ROS methods as incorporated in ProUCL 4.0, first predict k nondetect values using an OLS linear 

regression model (Chapter 4). This process yields a full data set of size n with (n-k) original detected 

values, and k new predicted (extrapolated) values. For ROS methods (normal, gamma, lognormal), 

ProUCL 4.0 generates additional columns consisting of (n-k) detected values, and predicted values of the 

k nondetect observations for each variable selected by the user. Once, the nondetect observations have 

been estimated, an experienced user may use any of the UTL computation methods for full data sets as 

described in Chapter 3 and incorporated in ProUCL 4.0. Those estimation methods are not repeated here. 

An example illustrating the use of ROS methods to estimate BTVs is described as follows. 

5.5 Estimating BTVs Using Nonparametric Methods Based Upon Higher 

Order Statistics 

It is noted that for full data sets without any discernable distribution, nonparametric UTLs and UPLs are 

computed using higher order statistics. Therefore, when the data set consists of enough detected 

observations, and if some of those detected data are larger than all of the NDs and the detection limits, the 

UTLs, UPLs, and upper percentiles can be estimated by simple nonparametric methods as described in 

Chapter 3. That is, just as in full data sets, nonparametric UPLs, UTLs, and percentiles for left-censored 

data sets are also estimated by upper ordered statistics such as the largest or the second largest value, and 

so on. This nonparametric approach to compute UTLs and UPLs is also available in ProUCL 4.0. 

Since, nonparametric UTLs, UPLs, and upper percentiles are represented by higher order statistics (or by 

some value in between higher order statistic obtained using linear interpolation) in a data set, therefore, 

every effort should be made to make sure that those higher order statistics do not represent contaminating 

outlying observations potentially coming from population(s) other than the background population under 

study. Nonparametric UTLs, UPLs, and percentiles should be used with caution to estimate the BTVs and 

compliance limits. Every effort should be made to identify and separate the outlying observations before 

computing order statistics-based upper limits to estimate the BTVs. 

5.5.1 Using Ad hoc Estimation Methods on Case-by-Case Basis 

This topic has also been discussed in Chapter 4. In order to be able to compute reasonable and reliable 

estimates using statistical methods, it is desirable to have enough detected values. Statistical methods are 

often based upon certain assumptions such as randomness, independence, and data distributions that are 

hard to verify and justify when dealing with small data sets having only a few (e.g., <4-6) detected values. 

Therefore, instead of using statistical methods on such small data sets, it is desirable to collect more 

samples (preferably using DQOs) from the area under investigation. If collection of more data is not 

feasible (e.g., due to lack of resources), it is desirable to use professional judgment, historical information, 

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information from similar sites, and all expert site knowledge to estimate the parameters of interest such as 

the EPC term, BTV, or a compliance limit. For an example when the number of detected values is less 

than 4-6, or when the majority of the data are NDs (e.g., > 80%, > 90%), then the compliance limit or the 

BTV based upon such data sets also represents a nondetect. In such cases, the BTVs or compliance limits 

may be determined using information from similar sites or by action levels determined by state or federal 

agencies. 

5.5.2 Example 1 

This data set of size 54 was also considered in Chapter 5 of the revised background guidance document. 

In this example, some data values have been treated as nondetect values with DL= 0.02. The example is 

used here to illustrate how one can identify high outliers in data sets consisting of some ND values in the 

lower tail of the data distribution. Typically, one is interested in identifying outliers in the right tail of the 

data distributions, the same outlier identification procedures (e.g., Rosner test) as used on full data sets 

can be used to identify outliers in data sets with ND values. The outlier procedures for data sets with and 

without outliers (as available in ProUCL 4.0) are described in Chapter 7. In the present example, the data 

set has at least one obvious outlier (= 130,000) as can be seen in Figure 5-1. From Figure 5-2, it is 

observed that 19,000 may also be an outlier, which got masked (in Figure 5-1) by the occurrence of the 

outlier, 130,000. Outlier, 130,000 was removed from the data set, and Rosner test was performed to 

identify more outliers that may be present in the data set. The Rosner test identified at least two more 

(with 2 suspected outliers) outlying observations as described in Table 5-1. 

Figure 5-1. Q-Q Plot of Aroclor Data Set 

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Figure 5-2. Q-Q Plot of Aroclor Data Set without Outlier 130,000 

Below are shown the results of the Rosner’s test performed on the data set without using nondetect (ND) 

observations (as one is interested to identify high outlying observations potentially representing hot 

spots). The two suspected outliers are 19,000 and 8,300. The test values (5.27 and 3.59) are considerably 

higher than the critical values at 5% significance level. 

Table 5-1. Rosner’s Outlier Test for Aroclor1254 (Excluding NDs and Outlier 130,000) 

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In order to illustrate the influence of the two highest outliers (19,000 and 8,300) on the computation of 

background statistics, the background statistics (for data sets with NDs) as described in this chapter (and 

in ProUCL 4.0) have been computed using data set with outliers (Table 5-2) and without the two outliers 

shown in Table 5-3. 

Table 5-2. Background Statistics for Aroclor 

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126 
Table 5-3. Background Statistics for Aroclor Data Set without Outliers 19,000 and 8,300 

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A quick comparison of statistics as summarized in Table 5-2 and Table 5-3 reveals that the inclusion of 

two outliers distorted all parametric and nonparametric estimates. For an example, for data set with 

outliers, a 95% UPL based upon KM estimates is 33,426, where as the corresponding 95% KM UPL 

without the two outliers is 4088. The project team should decide which of the two statistics represents a 

more accurate, reasonable, and realistic estimate of the background threshold value. 

Furthermore, in order to illustrate the influence of outliers on various other nonparametric limits based 

upon ranks (e.g., UPLs, UTLs), those nonparametric upper limits (as in ProUCL 4.0) on data sets with 

(Table 5-4) and without (Table 5-5) outliers are summarized in the following two tables. 

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Table 5-4. Nonparametric Background Statistics for the Data Sets with the Outliers 

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Table 5-5. Nonparametric Background Statistics for the Data Sets without the Outliers 

5.6 Minimum Sample Size Requirements 

These issues have been discussed throughout this guide and included here for convenience. Estimates of 

the various population (e.g., background area) parameters (e.g., not-to-exceed value) are obtained based 

upon sampled data collected from the population of interest. Determination of the adequate sample size 

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needed to obtain reliable estimates is perhaps the most important decision that one has to make when 

dealing with data sets with BDL observations. For data sets with and without NDs, some guidance about 

the minimum sample size requirements has been described in earlier chapters (e.g., Chapter 1, Chapter 4). 

For convenience, some of those recommendations and suggestions for data sets with NDs have been 

summarized below. 

It is noted that even though, most of the statistics (including fitting a linear regression line) used to 

estimate the EPC terms and BTV estimates could be computed based upon data sets even with 2 detected 

values; the use of those statistics is not recommended due to high level of uncertainty associated with 

them. It is re-emphasized that when only a few (e.g., < 4-6) observations are detected, it is not appropriate 

(due to high uncertainty associated with such estimates) to replace the rest (majority) of the nondetect 

data by their respective DL/2 values (or some other proxy value), and compute an estimate of the EPC 

term or BTV based upon the resulting data set (Singh, Maichle, and Lee (2006)). 

In case, only a small number (e.g., < 4-6) of detected observations are available, it is desirable to collect 

more samples and data (from the area under investigation), so that enough detected observations will be 

available to compute reasonably reliable estimates the various environmental parameters including BTVs 

and EPC terms. If it is not possible to collect more samples (as often is the case), it is suggested to use 

professional judgment and available historical information (e.g., from similar sites) to estimate the BTVs 

rather than using a statistical method on a fabricated data set obtained using some substitution method 

(e.g., DL/2). In such cases, BTVs and EPC terms may be estimated on case-by-case basis perhaps based 

upon the site knowledge (and information from similar sites) and experts’ opinions. 

For data sets consisting of mostly of nondetects, the EPC term or BTV may be estimated using simple ad 

hoc methods. Specifically, when all of the background sample data values are reported as nondetects, the 

BTV or a not-to-exceed value should also be reported as a nondetect value (or some pre-specified action 

level), perhaps by the maximum RL or maximum RL/2. 

Also, when only a few detected values (< 4-6) are available in a data set of larger size (e.g., > 15-20), one 

may again use the maximum RL, or maximum RL/2 as an estimate of the BTV or some pre-specified 

action level. When only a few detected values (e.g., < 4-6) are available in data sets of smaller sizes (e.g., 

< 8-10), one may use the maximum detected data value or the second largest detected value to estimate 

the BTV. 

Note: The uncertainty associated with all such estimates listed above will be high; and statistical 

properties such as bias, accuracy, and precision of such estimates would remain unknown. It is suggested 

to avoid using statistical methods (to estimate the BTVs and other environmental parameters) on data sets 

with less than 4-6 detected values (more are preferred). If possible, it is desirable to collect more samples 

(data) with detected values. Statistics computed based upon small data sets or on data sets with only a 

few detected values (e.g., < 4-6) cannot be considered reliable enough to make important remediation 

and cleanup decisions potentially affecting the human health and the environment. 

5.7 Additional Suggestions and Comments 

• It is noted that a linear model (regression line) can be obtained even when only two (2) 

detected observations are available. Therefore, the methods as discussed here and also 

incorporated in ProUCL 4.0 can be used on data sets with two or more detected 

observations. Obviously, in order to be able to compute reliable estimates of nondetects 

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and to compute defensible upper limits, enough detected observations should be made 

available. 

• If the use of appropriate data quality objectives (e.g., USEPA (2006)) is not possible to 

collect enough observations, every effort should be made to obtain a sample with about 

8-10 detected observations. ProUCL 4.0 prints out a message when estimates (of mean, 

sd, BTVs) are obtained using data sets with not many detected (e.g., < 8-10) 

observations. For accurate and reliable results and estimates, whenever possible, at least 

8-10 (more are desirable) detected observations should be made available, especially 

when the percentage of NDs becomes greater than 40%, 50%, and so on. This is 

especially true when one wants to compute the upper limits (e.g., UPLs, UTLs, and 

UCLs) based upon bootstrap resampling methods. 

• Also, in order to be able to use bootstrap resampling methods, it is desirable that the data 

set has at least five unique observations. Otherwise, the bootstrap procedures may result 

is resamples with all identical values. 

• It is important to point out that in practice, it is not reasonable or justifiable to assume or 

expect that the NDs should follow the same distribution as assumed for the detected 

values. This is especially true when multiple detection limits may be present, and the % of 

NDs may be quite high (e.g., > 40%). Additionally, ROS methods often yield infeasible 

predicted NDs such as negative values, or greater than the respective detection limits. 

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 Chapter 6 

Single and Two-Sample Hypotheses Testing Approaches as 

Incorporated in ProUCL 4.0 

Both single sample and two-sample hypotheses testing approaches are used to make cleanup decisions at 

polluted sites, and also to compare contaminant concentrations of two (e.g., site versus background) or 

more (several monitoring wells (MWs)) populations. This chapter briefly discusses some guidance on 

when to use single sample hypothesis test and when to use two-sample hypotheses approaches. These 

issues were also discussed in Chapter 1 of this Technical Guide. This chapter presents brief description of 

mathematical formulations of various parametric and nonparametric hypotheses testing approaches as 

available in ProUCL 4.0. ProUCL 4.0 also provides hypotheses testing approaches for data sets with 

nondetect (ND) observations. The details of those approaches can also be found in EPA (2006). 

Note: As discussed in Chapter 1, it should be pointed out that while developing ProUCL 4.0, emphasis is 

given to the practical applicability of the estimation and hypotheses testing methods as incorporated in 

ProUCL 4.0. It should also be noted that ProUCL 4.0 provides many graphical and statistical methods 

often used in the various statistical applications. ProUCL 4.0 assumes that the user (project team) has 

collected appropriate amount of good quality data, perhaps based upon DQOs. Consequently, ProUCL 

4.0 does not deal with statistical methods that may be used to compute sample sizes based upon 

performance measures and DQO processes (EPA, 2006). Those sample size determination methods have 

been discussed in detail in other EPA documents (EPA, 1997, EPA 2006). Moreover, some freeware 

packages such as VSP (2005) and DataQUEST (EPA, 1997) are also available that may be used to 

compute DQOs-based sample sizes from the populations under study. However, as mentioned before, 

some practical guidance on the minimum sample size requirements to be able to use methods as available 

in ProUCL 4.0 has been provided in Chapter 1. Similar statements and suggestions have been made in 

some other chapters (e.g., Chapters 4 and 5) of this Technical Guide. 

6.1 When to Use Single Sample Hypotheses Approaches 

When pre-established BTVs and not-to-exceed values are used, such as the USGS background values 

(Shacklette and Boerngen (1984)), thresholds obtained from similar sites, pre-established thresholds and 

not-to-exceed values, PRGs, or RBCs, there is no need to extract, establish, or collect a background or 

reference data set. When the BTVs and cleanup standards are known, one-sample hypotheses are used to 

compare site data (provided enough site data are available) with known and pre-established threshold 

values. It is suggested that the project team determine (e.g., preferably using appropriate DQOs) or decide 

(depending upon resources) about the number of site observations that should be collected and compared 

with the “pre-established” standards before coming to a conclusion about the status (clean or polluted) of 

the site area (e.g., RU, AOC) under investigation. When the number of available detected site samples is 

less than 4-6, one might perform point-by-point site observation comparisons with a BTV; and when 

enough detected site observations (> 8 to 10, more are preferable) are available, it is desirable to use 

single sample hypothesis testing approaches. Some of these issues have also been discussed in Chapter 1 

of this Technical Guide. 

Depending upon the parameter (e.g., the average value, μ0, or a not-to-exceed value, A0), represented by 

the known threshold value, one can use single sample hypothesis tests for population mean (t-test, sign 

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test) or single sample tests for proportions and percentiles. Several single sample tests listed as follows 

are available in ProUCL 4.0. 

One-Sample t-Test: This test is used to compare the site mean, μ, with some specified cleanup standard, 

Cs (or C ), where the cleanup standard, Cs or C represents a specified value of the true average threshold 

value, μ. The Student’s t- test (or a UCL of mean) is often used (assuming normality of site data, or when 

site sample size is large such as larger than 30, 50, 100) to determine the attainment of cleanup levels at a 

polluted site, perhaps after some remediation activities performed at the site. Note that the large sample 

size requirement (30, 50, 100, ...) depends upon the data skewness. Specifically, as skewness increases 

(measured in terms of sd of log-transformed data), the large sample size requirement also increases to be 

able to use central limit theorem (CLT). 

One-Sample Sign Test or Wilcoxon Signed Rank (WSR) Test: These tests are nonparametric tests and can 

also handle nondetect observations provided all nondetects (e.g., associated detection limits) fall below 

the specified threshold value, Cs(C). These tests are used to compare the site location (e.g., median, mean) 

with some specified cleanup standard, Cs or C representing the similar location measure. 

One-Sample Proportion Test or Percentile Test: When a specified cleanup standard, A0, such as a PRG, 

BTV, compliance limit, or a not-to-exceed value represents an upper threshold value of a contaminant 

concentration distribution (e.g., not-to-exceed value, compliance limit) rather than the mean threshold 

value, μ, of the contaminant concentration distribution, then: 

A test for proportion or a test for percentile (or equivalently a UTL 95%-95%, UTL 95%-90%) might be 

used to compare the site proportion, p of exceedances of the action level, A0 by site observations to some 

pre-specified allowable proportion, P0 of exceedances of A0 by site observations (perhaps after some 

remediation activity). It is noted that the proportion test can also handle NDs provided all NDs are below 

the action level, A0. A test for single sample proportion has been incorporated in ProUCL 4.0. 

As always, it is desirable to use a sampling plan based upon a DQO process to collect appropriate amount 

of detected data. In any case, in order to obtain reasonably reliable estimates and compute reliable test 

statistics, an adequate amount of representative site data (at least 8 to 10 detected observations) should be 

made available to perform single sample hypotheses tests listed above. 

Note: As mentioned before, in case only a few (e.g., < 4 to 6) detected site observations are available, 

instead of using hypotheses testing approaches, point-by-point site concentrations may be compared with 

the specified action level, A0. It should be noted that individual point-by-point observations are not 

compared with the average cleanup or threshold level, C or Cs . 

6.2 When to Use Two-Sample Hypotheses Testing Approaches 

When BTVs, not-to-exceed values, and other cleanup standards are not available, then site data are 

compared directly with the background data. In such cases, a two-sample hypothesis testing approach can 

be used to perform site versus background comparisons provided enough data are available from each of 

the two populations. Note that this approach can be used to compare concentrations of any two 

populations including two different site areas or two different monitoring wells (MWs). In order to use 

and perform a two-sample hypothesis testing approach, enough data of good quality should be available 

(collected) from each of the two populations under investigation. Site and background data requirements 

(e.g., based upon DQOs) to perform two-sample hypothesis test approaches are described in EPA (2006), 

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Breckenridge and Crockett (1995), and the VSP (2005) software package. Some minimum sample size 

requirements are provided in Chapter 1 of this Technical guidance document. 

While collecting site and background data, for better representation of populations under investigation, 

one may also want to account for the size of the background area (and site area for site samples) into 

sample size determinations. That is, a larger number (>10 to 15) of representative background (or site) 

samples should be collected from larger background (or site) areas. As mentioned before, every effort 

should be made to collect as many samples as determined using DQO processes as described in EPA 

documents (2006). 

The two-sample (or more) hypotheses approaches are used when the site parameters (e.g., mean, shape, 

distribution) are being compared with the background parameters (e.g., mean, shape, distribution). The 

two-sample hypotheses testing approach is also used when the cleanup standards or screening levels are 

not known a priori, and they need to be estimated based upon a data set from a background or reference 

population. Specifically, two-sample hypotheses testing approaches are used to compare: 1) the average 

(also medians or upper tails) contaminant concentrations of two or more populations such as the 

background population and the potentially contaminated site areas, or 2) the proportions of site and 

background observations exceeding a pre-established compliance limit, A0. In order to derive reliable 

conclusions with higher statistical power based upon hypothesis testing approaches, enough data (e.g., 

minimum of 8 to 10 samples) should be available from all of the populations under investigation. 

It is desirable and recommended to always supplement statistical methods and test statistics with 

graphical displays, such as the double Q-Q plots, or side-by-side multiple box plots, as available in 

ProUCL 4.0. Several parametric and nonparametric two-sample hypotheses testing approaches, including 

Student’s t-test, the Wilcoxon-Mann-Whitney (WMW) test, Gehan’s test, and the quantile test are 

included in ProUCL 4.0. Some details of those methods are described in this chapter. It should be noted 

that the WMW, Gehan, and quantile tests can also be used on data sets with NDs. Gehan’s test is 

specifically meant to be used on data sets with multiple detection limits. It is suggested that for best and 

reliable conclusions, both WMW test and quantile tests should be used in parallel on the same data set. 

The details of these two tests with examples are given in EPA (1994, 2006). 

The samples collected from the two (or more) populations should all be of the same type obtained using 

similar analytical methods and apparatus. In other words, the collected site and background samples 

should be all discrete or all composite (obtained using the same design and pattern), and be collected from 

the same medium (soil) at similar depths (e.g., all surface samples or all subsurface samples) and time 

(e.g., during the same quarter in groundwater applications) using comparable (preferably same) analytical 

methods. Good sample collection methods and sampling strategies are given in EPA (1996, 2003) 

guidance documents. 

Note: As mentioned before, it is noted that ProUCL 4.0 does not deal with DQOs and sample size 

determinations for the various statistical applications. There are other free software packages (e.g., VSP, 

2005, and DataQUEST, 1997) available that may be used to compute DQO-based sample sizes for the 

populations under investigations. In ProUCL 4.0, emphasis is given to the practical applicability of 

statistical and graphical methods that may be used to make sense of data collected from the various 

environmental applications and studies. Specifically, it is assumed that the user has collected adequate 

amount of data of good quality (perhaps based upon appropriate DQOs) to be able to use statistical 

methods as available in ProUCL 4.0. 

135 
In order to familiarize the users with the statistical terminology used in all hypotheses testing approaches 

as incorporated in ProUCL 4.0, a brief discussion of the various terms used is described next. A brief 

discussion of the error rates and associated sample sizes is also included in the following. Detailed 

descriptions of these terminologies can be found in EPA (2002, 2006). 

6.3 Statistical Terminology Used in Hypotheses Testing Approaches 

The first step in developing a hypothesis test is to transform the problem into statistical terminology by 

developing a null hypothesis, H0, and an alternative hypothesis, HA. These hypotheses tests result in two 

alternative decisions (acceptance of the null hypothesis or the rejection of the null hypothesis) that a 

hypothesis test statistic (e.g., t-statistic, WMW test statistic) will evaluate and determine. In this section, 

these terminologies (e.g., error rates, hypotheses statements, Form 1, Form2, two sided tests) are 

explained in terms of two-sample hypotheses testing approaches often used to compare background and 

site parameters and data distributions. Similar terminology applies to all parametric and nonparametric 

hypotheses testing approaches include the single sample and two-sample hypotheses testing approaches, 

as incorporated in ProUCL 4.0. 

In two-sample comparisons such as background area versus potentially impacted site area comparisons, 

the parameter of interest is symbolized by the Greek letter, delta (Δ), the amount by which the mean of 

the distribution of concentrations in potentially impacted areas exceeds the mean of the background distribution. Delta is an unknown parameter, and statistical tests may be used to evaluate hypotheses relating to 

its possible values. The statistical tests are designed to reject or not reject hypotheses about Δ based on 

test statistics computed based upon sampled data collected from the background and the impacted site 

area(s). 

The action level for background comparisons is the largest value of the “difference in means” that is 

acceptable to the decision maker. In this guidance, the action level for the difference (site mean - 

background mean) in means is defined as a substantial difference, S, which may be zero or a positive 

value based on the risk assessment, an applicable regulation, a screening level, or guidance. In some 

cases, the largest acceptable value for the difference in means may be S = 0. Typically, the value for “S” 

is determined on a case-by-case basis and the analyte under comparison. Some discussion on the selection 

of the substantial difference, S, is given in Appendix A of the Guidance for Comparing Background and 

Chemical Concentrations in Soil for CERCLA Sites (EPA, 2002b). It should be noted that whenever 

applicable, ProUCL 4.0 provides an option to select a suitable value of S while testing for Form 2 null 

hypotheses defined below. If the user is not very sure about the selection for a value of the substantial 

difference, S, the user may perform the test for more than one value of S. If deemed necessary, the user 

may perform sensitivity analyses by using ProUCL 4.0 for several values of S. 

Estimates of Δ are obtained by measuring the differences in contaminant concentrations in potentially 

impacted areas and in background areas. For example, one estimate of the mean concentration in 

potentially impacted areas is the simple arithmetic average of measurements from those site areas. An 

estimate of the mean background concentration is similarly calculated. An estimate of the difference in 

population means, Δ, is obtained by subtracting the mean background concentration from the mean 

concentration in potentially impacted areas. In most cases of interest, the estimate of Δ will be a positive 

number. If there is little or no contamination on the site, then the estimate for Δ may be near zero or 

slightly negative. Note that the estimated value for Δ calculated by using this simple procedure (or by 

some more complicated procedure) only represents an estimate of the true value of Δ. Hence, decisions 

based on any estimated value for Δ may be incorrect due to uncertainty and variability associate with its 

estimated value. 

136 
Adopting hypothesis tests and an appropriate data quality objective (DQO) approach (EPA, 2006) can 

control the probability of making decision errors. However, incorrect use of hypothesis tests can lead to 

erratic decisions. Each type of hypothesis test is based on a set of assumptions that should be verified to 

confirm proper use of the test. Procedures and assumptions for determining the selection and proper use 

of parametric tests (such as a t-test), and nonparametric test (e.g., WMW test) are provided in (EPA, 

2006). It is pointed out that some goodness-of-fit (GOF) tests and graphical procedures are available in 

ProUCL 4.0 to verify the distributional assumptions. 

Hypothesis testing is a quantitative method to determine whether or not a specific statement (called the 

null hypothesis) concerning Δ can be rejected. Decisions concerning the true value of Δ reduce to a choice 

between “yes” or “no.” When viewed in this way, two types of incorrect decisions, or decision errors, 

may occur: 

Incorrectly deciding the answer is “yes” when the true answer is “no”; and 

Incorrectly deciding the answer is “no” when the true answer is “yes.” 

While the possibility of decision errors can never be completely eliminated, it can be controlled. To 

control decision errors, it is necessary to control the uncertainty in the estimate of Δ. It should be noted 

that the uncertainty typically arises from three sources: 

1. Sampling error; 

2. Measurement error; and 

3. Natural site variability. 

The decision maker has some control of the first two sources of uncertainty. For example, a larger number 

of samples may lead to fewer decision errors because the probability of a decision error decreases as the 

number of samples increases. Use of more precise measurement techniques or duplicate measurements 

can reduce measurement error, thus minimizing the likelihood of a decision error. The third source of 

uncertainty is more difficult to control. However, the site variability may also be controlled to some 

extent if more rigorous sampling procedures are used to collect samples. Some good sample collection 

methods and sampling strategies (e.g., based upon Gy sampling theory) are given in EPA (1996, 2003) 

guidance documents. 

Natural variability arises from the uneven distribution of chemical concentrations on the site and in background areas. Natural variability is measured by the true standard deviation, σ of the concentration 

distribution. A large value of σ indicates that a large number of measurements will be needed to achieve a 

desired limit on decision errors. Since variability is usually higher in impacted areas of the site than in 

background locations, data collected from the site is used to estimate σ. An estimate for σ frequently is 

obtained from historical data, if available. Estimates of variability reported elsewhere at similar sites with 

similar contamination problems may also be used. If an estimate of the mean concentration in 

contaminated site areas is available, then the coefficient of variation (CV) observed at other sites may be 

multiplied by the mean to obtain an estimate the standard deviation (sd). If no acceptable historical source 

for an estimate of σ is available, then it may be necessary to conduct a small-scale pilot survey on site 

using 20 or more random samples to estimate σ. Due to the small sample size of the pilot, it is advisable 

to use an 80 or 90 percent upper confidence limit for the estimate of σ rather than an unbiased estimate to 

137 
avoid underestimating the true variability. A crude estimate of σ is obtained by dividing the anticipated 

range (maximum - minimum) by number, 6. 

The hypothesis testing process provides a formal procedure to quantify the decision maker’s acceptable 

limits for decision errors. The decision maker’s limits on decision errors are used to establish performance 

goals for data collection that reduce the chance of making decision errors of both types. The gray region 

is a range of possible values of Δ where the consequences of making a decision error (of both type) are 

relatively minor. Specifically, a reasonable statistical test should have a low probability of reflecting a 

substantial positive (> 0) difference, S, when the site and background distributions are comparable (false 

positive), but has a high probability of reflecting a substantial difference when the concentrations in 

potentially impacted areas greatly (significantly) exceed the background concentrations. In the gray 

region between these two extremes, the statistical test has relatively poor performance. When the test 

procedure is applied to a site with a true mean concentration in the gray region, the test may indicate that 

the site exceeds background, or may indicate that the site does not exceed background, depending on 

random fluctuations in the sample. 

It is necessary to specify a gray region for the test because the decision may be “too close to call” due to 

uncertainty in the estimate of Δ. This may occur when the difference in means is small compared to the 

minimum detectable difference (MDD) for the test. The minimum detectable difference (MDD) is the 

smallest difference in means that the statistical test can resolve. The MDD depends on sample-to-sample 

variability, the number of samples, and the power of the statistical test. In the gray region, the uncertainty 

in the measurement of Δ is larger than the difference between Δ and the action level, so it may not be 

possible for the test to yield a correct decision with a high probability. One step in the hypothesis test 

procedure is to assign upper bounds on the decision error rates for values of Δ above and below the gray 

region. These bounds limit the probability of occurrence of decision errors. 

Typically, the gray region depends upon the type of hypothesis test that is selected by the decision maker. 

In general, the gray region for Δ is to the right of the origin (Δ = 0) and bounded from above by the 

substantial difference (Δ = S). Some guidance on specifying a gray region for the test is available in 

Chapter 6 of Guidance for the Data Quality Objectives Process (EPA, 1994). The size of the gray region 

may also depend on specific regulatory requirements or policy decisions that may not be addressed in the 

DQO guidance. 

The width of the gray region is called the “minimum Table 6-1. Required Sample Sizes for Selected 

detectable difference” for the statistical test, indica- Values of σ (α = β = 0.10 and MDD = 50 mg/kg) 

ting that differences smaller than the MDD cannot 

be detected reliably by the test. If a test is used to σ

determine if concentrations of the potentially impac- (mg/kg) MDD/σ n N 

ted areas exceed background concentrations by more 25 2 3.7 5 than S units, then it is necessary to ensure that the 

MDD for the test is less than S. In the planning 50 1 13.55 16 

stage, this requirement is met by designing a samp- 75 0.67 29.97 35 

ling plan with sufficient power to detect differences 100 0.5 52.97 62 

as small as S. 125 0.4 82.53 96 

Note: If possible, it is suggested that if data were 150 0.33 118.66 138 

collected without the benefit of a sampling plan 175 0.29 161.36 188 

(e.g., with specified decision errors),then the 200 0.25 210.63 245 

practitioners or the project team may want to 

138 
perform retrospective power calculation of the test before making a decision about the acceptance or 

rejection of the null hypothesis under consideration. 

It is also noted that the use of graphical displays (e.g., side-by-side box plots, multiple Q-Q plots) provide 

added insight and information about the comparability of data sets that may not be possible to observe 

and assess based upon simple test statistics such as t-test or Gehan’s test statistic. 

In the planning stage, the absolute size of the MDD is of less importance than the ratio of the MDD to the 

natural variability of the contaminant concentrations in the potentially impacted area. This ratio is termed 

the “relative difference” and defined as MDD/σ, where σ is an estimate of the standard deviation of the 

distribution of concentrations on the site. The relative difference expresses the power of resolution of the 

statistical test in units of uncertainty. Relative differences much less than one standard deviation (MDD/σ

< 1) are more difficult to resolve unless a larger number of measurements are available. Relative 

differences of more than three standard deviations (MDD/σ > 3) are easier to resolve. As a general rule, 

values of MDD/σ near 1 will result in acceptable sample sizes. The required number of samples may 

increase dramatically when MDD/σ is much smaller than one. Conversely, designs with MDD/σ larger 

than three may be inefficient. If MDD/σ is greater than three, then additional measurement precision is 

available at a minimal cost by reducing the width of the gray region. The cost of the data collection plan 

should be examined quantitatively for a range of possible values of the MDD before selecting a final 

value. A tradeoff exists between cost (number of samples required) and benefit (better power of resolution 

of the test). 

The number of measurements required to achieve the 

specified decision error rates has an inverse Table 6-2. Achievable Values of α = β for 

relationship with the value of MDD/σ. An example of Selected Values of N with MDD/ σ = 0.5 

this inverse relationship is demonstrated in Table 6-1 N n z1-α α = β for hypothetical values of α = β = 0.10 and MDD = 50 

mg/kg. Sample sizes may be obtained using the 10 8.62 0.517 0.303 

approximate formula given in EPA (2006), written 15 12.93 0.633 0.263 

here as: 

20 17.24 0.731 0.232 

n = (0.25) z

2

1-α + 2 (z1-α + z1- β )

2 

σ

2

 /(MDD)2, 25 21.55 0.817 0.207 

30 25.86 0.896 0.185 

Here zp is the pth percentile of the standard normal 40 34.48 1.034 0.151 

distribution. Note the inverse-squared dependence of n 50 43.1 1.156 0.124

on MDD/σ. The smaller values of α and β (leading to 

larger values for the z terms) magnify the strength of 60 51.72 1.266 0.103 

this inverse relationship. A recommended sample size 70 60.34 1.368 0.086 

of N = (1.16)*n is tabulated for a variety of σ values in 100 86.21 1.635 0.051 

the table. Note the dramatic increase in the sample size 150 129.31 2.002 0.023

as the value of MDD/σ is lowered from 1 to 0.25. 

200 172.41 2.312 0.01 

Letting α = β, we can solve for z1-α = z1- β: 

z

2

1-α = n / [0.25 + 8 σ

2

 / (MDD)2 ]. 

For any fixed value of MDD/σ, the decision error rate, α, is a function of n: 

α = 1 - Φ[z1-α (n)], 

139 
Here Φ is the cumulative normal distribution function. Achievable values of α (and β) for selected sample 

sizes with a hypothetical value of MDD/σ = 0.5 are shown in Table 6-2. 

A tradeoff analysis should begin with the analysis of the choice MDD = S, where S is a substantial 

difference. Note that a choice of MDD > S would lead to a sample size that does not have sufficient 

power to distinguish a difference between the site and background means as small as S. Hence, the 

minimum acceptable number of samples for the decision is obtained when MDD = S. If S/σ is less than 

one, then this indicates that MDD/σ is also less than one, and a relatively large number of samples will be 

required to make the decision. If S/σ exceeds three, then a reasonably small number of samples are 

required for this minimally acceptable test design. More information about the choice of MDD and S can 

be found in EPA (2002). 

Two forms (Form 1 and Form 2) of the statistical hypothesis test are useful for many environmental 

applications. In addition to testing the two forms of hypotheses testing, ProUCL 4.0 can also be used to 

perform two-sided hypotheses tests. In this section, these forms of hypotheses are described for twosample hypotheses tests often used to compare site concentrations with background concentrations. The 

null hypothesis in the first form (Form 1) of the test states that there is no statistically significant 

difference between the means of the concentration distributions measured at the site and in the selected 

background areas. The null hypothesis in the second form (Form 2) of the test is that the concentrations of 

the impacted site exceed the background concentrations by a substantial difference, S. Both forms of the 

null hypothesis are described next. 

6.3.1 Test Form 1 

The null hypothesis for background comparisons, “the concentrations in potentially impacted sites areas 

do not exceed (or are less than equal) background concentrations,” is formulated for the express purpose 

of being rejected. 

• The null hypothesis (H0): The mean contaminant concentration in samples collected from 

potentially impacted areas is less than or equal to the mean concentration in samples 

collected from background areas (H0: Δ ≤ 0). 

• The alternative hypothesis (HA). The mean contaminant concentration in samples from 

potentially impacted areas is greater than the mean in background areas (HA: Δ > 0). 

When using this form of hypothesis test, the collected data should provide statistically significant 

evidence that the null hypothesis is false leading to the conclusion that the site mean does exceed background mean concentration. Otherwise, the null hypothesis cannot be rejected based on the available data, 

and the concentrations found in the potentially impacted areas are considered equivalent and comparable 

those of the background. 

Two serious problems arise when using the Background Tests Form 1. One type of problem arises when 

there is a very large amount of data. In this case, the MDD for the test will be very small, and the test may 

reject the null hypothesis when there is only a very small difference between the site and background 

mean concentrations. If the site exceeds background by only a small amount, then there is a very high 

probability that the null hypothesis will be rejected if a sufficiently large number of samples were taken. 

This case can be avoided by selecting the Background Test Form 2, which incorporates an acceptable 

level for the difference between the site and the background concentrations (see Section 6.3.2). 

140 
A second type of problem may arise in the use of the Background Test Form 1 when insufficient data are 

available. This may occur, for example, when the onsite or background variability was underestimated in 

the design phase. An estimated value for σ is used during the preliminary phase of the DQO planning 

process to determine the required number of samples. When the samples are actually collected, σ can then 

be re-estimated, and the power of the analysis should be re-evaluated. If the variance estimate used in the 

planning stage was too low, the statistical test is unlikely to reject the null hypothesis due to the lack of 

sufficient power. 

Hence, when using the Background Test Form 1, if possible (if resources are available), it is desirable to 

conduct a retrospective power analysis to ensure that the power of the test was adequate to detect a site 

area with a mean contamination that exceeds the background by more than the MDD. A procedure to 

perform this is to re-compute the required sample size using the sample variance in place of the estimated 

variance that was used to determine the required sample size in the planning phase. If the actual sample 

size is greater than this post-calculated size, then it is likely that the test has adequate power. 

Alternatively, if the retrospective analysis indicates that adequate power was not obtained, it may be 

necessary to collect more samples. Hence, if large uncertainties exist concerning the variability of the 

contaminant concentration in potentially impacted areas, the Background Test Form 1 may lead to 

inconclusive results. Therefore, the sample size should exceed the minimum number of samples required 

to give the test sufficient power. 

Note: In cases, when it is not possible to collect enough samples, one should also use graphical displays 

to compare site concentrations with background concentrations to gain additional information about the 

populations under comparison. It should be noted that a minimum of at least 8-10 observations should be 

made available to include and use graphical displays in the decision making processes regarding site 

versus background comparisons. 

6.3.2 Test Form 2 

In the Background Test Form 2, the null hypothesis is stated as, “the concentration in potentially impacted areas exceeds background concentration.” 

• The null hypothesis (H0): The mean contaminant concentration in potentially impacted 

areas exceeds the background mean concentration by more than S units. Symbolically, 

the null hypothesis is written as H0: ∆ ≥ S. 

• The alternative hypothesis (HA): The mean contaminant concentration in potentially 

impacted areas does not exceed the background mean concentration by more than S (HA: 

∆ < S). 

Here, S is the background investigation level. Although there is no explicit use of the quantity S in the 

hypothesis statement used in the Background Test Form 1, an estimate of S is important for determining 

an upper limit for the MDD for the Background Test Form 1. The background investigation level, S, is 

determined on a case-by-case basis by the project team, EPA, and other stakeholders. ProUCL 4.0 can 

also be used to determine a value for S (by performing sensitivity analysis to determine a value of S). 

6.3.3 Selecting a Test Form 

The test forms described above are commonly used in background versus site comparison evaluations. 

Therefore, these test forms are also known as Background Test Form 1 and Background Test Form 2 

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(EPA, 2002). Background Test Form 1 uses a conservative investigation level of Δ = 0, but relaxes the 

burden of proof by selecting the null hypothesis that the contaminant concentrations in potentially 

impacted areas are not statistically greater than the background concentrations. Background Test Form 2 

requires a stricter burden of proof, but relaxes the investigation level from 0 to S. 

6.3.4 Errors Tests and Confidence Levels 

A key step in developing a sampling and analysis plan is to establish the level of precision required of the 

data (EPA (1994)). Whether a null hypothesis is rejected or not depends on the results of the sampling. 

Due to the uncertainties that result from sampling variation, decisions made using hypotheses tests will be 

subject to errors, also known as decision errors. Decisions should be made about the width of the gray 

region and the degree of decision errors that is acceptable. There are two ways to err when analyzing data 

(Table 6-3). 

• Type I Error: Based on the observed data, the test may reject the null hypothesis when in 

fact the null hypothesis is true (a false positive or equivalently a false rejection). This is a 

Type I error. The probability of making a Type I error is often denoted by α (alpha); and 

• Type II Error: On the other hand, the test may fail to reject the null hypothesis when the 

null hypothesis is in fact false (a false negative or equivalently a false acceptance). This is 

called Type II error. The probability of making a Type II error is denoted by β (beta). 

Table 6-3. Hypothesis Testing: Type I and Type II Errors 

Decision Based on 

Sample Data 

Actual Site Condition 

H0 is True H0 is not true 

H0 is not rejected Correct Decision: (1 – α) Type II Error: 

False Negative (β) 

H0 is rejected Type I Error: 

False Positive (α) Correct Decision: (1 – β) 

The acceptable level of decision error associated with hypothesis testing is defined by two key 

parameters – confidence level and power. These parameters are related to the two error probabilities, α

and β. 

• Confidence level 100(1 – α)%: As the confidence level is lowered (or alternatively, as α

is increased), the likelihood of committing a Type I error increases. 

• Power 100(1 – β)%: As the power is lowered (or alternatively, as β is increased), the 

likelihood of committing a Type II error increases. 

Although a range of values can be selected for these two parameters, as the demand for precision 

increases, the number of samples and the associated cost (e.g., sampling cost) will generally also increase. 

The cost of sampling is often an important determining factor in selecting the acceptable level of decision 

errors. However, unwarranted cost reduction at the sampling stage may incur greater costs later in terms 

of increased threats to human health and the environment, or unnecessary cleanup at a site area under 

investigation. The number of samples, and hence the cost of sampling, can be reduced but at the expense 

of a higher possibility of making decision errors that may result in the need for additional sampling, 

unnecessary remediation, or increased risk to the human health and the environment. 

142 
The selection of appropriate levels for decision errors and the resulting number of samples is a critical 

component of the DQO process that should concern all stakeholders. Because there is an inherent tradeoff 

between the probabilities of committing Type I or Type II error, a simultaneous reduction in both types of 

errors can only occur by increasing the number of samples. If the probability of committing a false 

positive error is reduced by increasing the level of confidence associated with the test (in other words, by 

decreasing,α), the probability of committing a false negative is increased because the power of the test is 

reduced (increasing β). 

Typically, the following values for error probabilities are selected as the minimum recommended 

performance measures (EPA, 1990 and EPA, 2002). 

• For the Background Test Form 1, the confidence level should be at least 80% (α = 0.20) 

and the power should be at least 90% (β = 0.10). 

• For the Background Test Form 2, the confidence level should be at least 90% (α = 0.10) 

and the power should be at least 80% (β = 0.20). 

It should be noted that when using the Background Test Form 1, a Type I error (false positive) is less 

serious than a Type II error (false negative). This approach favors the protection of human health and the 

environment. To ensure that there is a low probability of committing Type II errors, a Test Form 1 

statistical test should have adequate power at the right edge of the gray region. 

When the Background Test Form 2 is used, a Type II error is preferable to committing a Type I error. 

This approach favors the protection of human health and the environment. The choice of the hypotheses 

used in the Background Test Form 2 is designed to be protective of human health and the environment by 

requiring that the data contain evidence of no substantial contamination. 

6.4 Parametric Hypotheses Tests 

Parametric statistical tests assume that the data have a known distributional form. They may also assume 

that the data are statistically independent or that there are no spatial trends in the data. 

Many statistical tests and models are only appropriate for data that follow a particular distribution. 

Statistical tests that rely on knowledge of the form of the population distribution for the data are known as 

parametric tests, because the test is usually phrased in terms of the parameters of the distribution assumed 

for the data. The most important distribution for tests involving environmental data is the normal 

distribution. Till recent past, the use of a lognormal distribution has been quite common in many 

environmental applications. It is well known that the use of a lognormal distribution often results in 

unstable and impractical estimates of the EPC terms (e.g., Singh, Singh, and Iaci, 2002, and Singh, 

Maichle, and Lee, 2006) and t-test statistic used on log-transformed data. Some of these issues will be 

illustrated later in this chapter. 

Goodness-of-fit tests for data distribution (such as the Shapiro-Wilk test for normality) often fail if there 

are insufficient data, if the data contain multiple populations, or if there is a high proportion of nondetects 

in the collected data set. Tests for normality lack statistical power for small sample sizes. In this context, a 

sample consisting of less than 20 observations (EPA, 2002) may be considered a small sample. However, 

in practice, many times it is not possible and feasible (due to resource constraints) to collect data sets of 

sizes greater than 8-10. This is especially true for background data sets. Furthermore, the decision makers 

often do not want to collect many background samples, and they want to make cleanup decisions based 

upon data sets of sizes even smaller than 8. As discussed in Chapter 1, it again is suggested to avoid 

143 
deriving cleanup decisions (with potential effect on the human health and the environment) based upon 

data sets of small sizes such as smaller than 8. 

Note: Statistics computed based upon smaller data sets of sizes 4 to 5 cannot be considered reliable 

enough to derive important decisions affecting the human health and the environment. Every effort should 

be made by all parties involved including the project team and the decision makers to provide enough 

resources and budget so that adequate amount of data (perhaps based upon DQOs) can be collected from 

the various areas under investigation. 

6.5 Nonparametric Hypotheses Tests 

Statistical tests that do not assume a specific statistical form for the population distribution(s) are called 

distribution-free or nonparametric statistical tests. Nonparametric tests have good test performance for a 

wide variety of distributions, and their performances are not unduly affected by the outlying observations. 

Nonparametric tests can be used for normal or non-normal data sets. In two-sample comparisons, if one or 

both of the data sets fail to meet the test for normality, or if the data sets appear to come from different 

distributions, then nonparametric tests may be used to perform site versus background comparisons. 

Several two-sample nonparametric hypotheses tests (e.g., the WMW test, the Gehan test, the quantile test) 

are available in ProUCL 4.0. 

The relative performances of different testing procedures may be assessed by comparing, p-values 

associated with those tests. The p-value of a statistical test is defined as the smallest value of α (level of 

significance, Type I error) for which the null hypothesis would be rejected for the given set of 

observations. The p-value of a test is sometimes called the critical level or the significance level of the 

test. 

Performance of statistical tests is also compared based on their robustness. Robustness means that the test 

has good performance for a wide variety of data distributions, and that its performance is not significantly 

affected by the occurrence of outliers. It should be pointed out that not all nonparametric methods are 

robust and resistant to outliers. Specifically, nonparametric upper limits used to estimate BTVs and notto-exceed values can get affected and misrepresented by outliers. This issue has been discussed in Chapter 

3 of this Technical Guide. In addition, nonparametric tests used to compare population means and 

medians generally are unaffected by a reasonable number of nondetect values. There are other 

circumstances and situations that should be considered: 

• If a parametric test for comparing means is applied to data from a non-normal population 

and the sample size is large, then the parametric test may work well provided that the 

data sets are not heavily skewed. For heavily skewed data sets, the sample size 

requirement associated with central limit theorem (CLT) can become quite large such as 

larger than 100 (Singh, Singh, and Iaci, 2002, Singh and Singh, 2003). For moderately 

skewed (as defined in Chapter 4) data sets, the CLT ensures that parametric tests for the 

mean will work because parametric tests for the mean are robust to deviations from 

normal distributions as long as the sample size is large. Unfortunately, the answer to the 

question of how large is large enough depends on the nature of the particular distribution. 

Unless the population distribution is very peculiar (e.g., highly skewed), one may choose 

a parametric test for comparing means when there are at least 25-30 data points in each 

group. 

144 
• If a nonparametric test for comparing means is applied to data from a normal population 

and the sample size is large, then the nonparametric test will work well. In this case, the 

p-values tend to be a little too large, but the discrepancy is small. In other words, nonparametric tests for comparing means are only slightly less powerful than parametric tests 

with large samples. 

• If a parametric test is applied to data from a non-normal population and the sample size is 

small (for example, less than 20 data points), then the p-value may be inaccurate because 

the central limit theory does not apply in this case. 

• If a nonparametric test is applied to data from a non-normal population and the sample 

size is small, then the p-values tend to be too high. In other words, nonparametric tests 

may lack statistical power with small samples. 

In conclusion, large data sets do not present any problems. In this case, the nonparametric tests are 

powerful and the parametric tests are robust. However, small data sets are challenging. In this case, the 

nonparametric tests are not powerful, and the parametric tests are not robust. 

Note: It is re-stated that there is no substitute for visual graphical displays of data sets. The users should 

always supplement their findings and conclusions by using graphical displays for visual comparisons of 

two or more data sets. ProUCL 4.0 offers both side-by-side box plots and multiple Q-Q plots that can be 

used to graphically compare two or more populations. 

Having discussed the various terminologies associated with hypotheses testing approaches, some 

parametric and nonparametric single sample hypotheses testing approaches as incorporated in ProUCL 

4.0 are described in the next section. Some of these approaches can also handle data sets with ND 

observations. 

6.6 Single Sample Hypotheses Testing Approaches 

This section briefly describes the mathematical formulation of parametric and nonparametric single 

sample hypotheses testing approaches as incorporated in ProUCL 4.0. Some details of the single sample 

hypotheses tests can be found in EPA (2006). 

6.6.1 The One-Sample t-Test 

The one-sample t-test is a parametric test used for testing a difference between a population (site area, 

AOC) mean and a fixed pre-established threshold also representing a mean concentration level. Some 

minimum sample size requirements associated with this test have been described in Chapter 1. However, 

it is suggested that every effort should be made to collect adequate amount of data, perhaps using an 

appropriate DQO process. The collected sample should represent a random sample properly representing 

the site area or an area of concern (AOC) under investigation. 

6.6.1.1 Limitations and Robustness 

• This test is not robust in the presence of outliers. 

• Does not give reliable results in the presence of less than values. It is suggested not to use 

this test when dealing with data sets with NDs. Some other nonparametric tests described 

as follows may be used in case NDs are present in the samples data set. 

145 
• It may yield reliable results when performed on mildly or moderately skewed data sets. 

Note that skewness is discussed in Chapters 3 and 4. 

• Its use should be avoided when data are highly skewed, even when the data set is of a 

large size such as 100. 

6.6.1.2 Directions for the One-Sample t-Test 

Even though, the user can perform this test using ProUCL4.0, a brief description of the t-test is given in

this section. Let X1, X2, . . . , Xn represent a random sample of size, n, from the site area (an AOC, EA) 

under investigations. The following directions are written keeping in mind that the user may be manually

performing the test. It is pointed out ProUCL 4.0 computes all statistics and prints out the conclusion 

based upon the data set. 

STEP 1: Specify an average cleanup goal/level, μ0 (or C, Cs), and state the following null hypotheses (as 

available in ProUCL 4.0): 

Form 1: H0: site μ ≤ μ0 vs. alternative H1: site μ > μ0

Form 2: H0: site μ ≥ μ0 vs. alternative H1: site μ < μ0

Two Sided: H0: site μ = μ0 vs. alternative H1: site μ ≠ μ0. 

Form 2 with substantial difference, S: H0: site μ ≥ μ0 + S vs. alternative H0: site μ < μ0 + S, here S≥ 0. 

STEP 2: Calculate the test statistic: 

X − μ0 − S

t = (6-1) 0 sd 

n 

Note: In the above equation, S is assumed to be equal to “0”, except for Form 2 with substantial 

difference. 

STEP 3: Use a t-table (ProUCL 4.0 computes them) to find the critical values of t-statistic. 

Conclusion: 

Form 1: If t0 > tn-1, 1-α, then reject the null hypothesis that the site population mean is less than the cleanup 

level. 

Form 2: If t0 < -tn-1, 1-α, then reject the null hypothesis that the site population mean exceeds the cleanup 

level. 

Two Sided: If |t0 | > tn-1, 1-α/2, then reject the null hypothesis that the site population mean is same as the 

cleanup level. 

Form 2 with substantial difference, S: If t0 < -tn-1, 1-α, then reject the null hypothesis that the site population 

mean is more than the cleanup level, μ0 + the substantial difference, S. 

P-values 

A p-value is the smallest value for which the null hypothesis is rejected in favor of the alternative 

hypotheses. Thus, based upon the given data, the null hypothesis is rejected for all values of α (the level 

of significance) greater than or equal to the p-value. The details of computing a p-value for t- test can be 

found in any statistical text book such as Daniel (1995). ProUCL 4.0 computes p-values for t-test 

146 
associated with each form of null hypothesis. If the computed p-value is smaller than the specified value 

of, α, the conclusion is to reject the null hypothesis based upon the collected data set used in the various 

computations. 

6.6.2 The One-Sample Test for Proportions 

The one-sample test for proportions represents a test for a difference between the population proportion, 

P, and a fixed desired threshold proportion, P0. Based upon the sampled data set and sample proportion, 

p, of exceedances of an action level, A0, by the n site observations; the objective is to determine if the 

population proportion (of exceedances of a threshold value, A0) exceeds the pre-specified proportion 

level, P0. It is noted that this proportion test is equivalent to a sign test (described as follows), when P0 = 

0.5. 

6.6.2.1 Limitations and Robustness 

• Normal approximation is applicable when both (nP0) and n (1- P0) are at least 5. 

• For smaller data sets, ProUCL 4.0 uses exact binomial distribution (e.g., Conover, 1999) 

to compute the critical values when the above statement is not true. 

• The proportion test may also be used on data sets with ND observations (also available in 

ProUCL 4.0), provided all NDs are smaller than the action level, A0. 

6.6.2.2 Directions for the One-Sample Test for Proportions 

Let X1, X2, . . . , Xn represent a random sample of size, n, from the site (AOC, EA) under investigation. 

Let A0 represents a compliance limit or a threshold level to be met by site data (perhaps after some 

remediation activity). It is expected (e.g., after remediation) that the proportion of site observations 

exceeding the compliance limit, A0, is smaller than the specified proportion, P0. 

Let B = # of site values in the sample exceeding the compliance limit, A0. A typical observed sample 

value of B (based upon a data set) is denoted by b. It is noted that B follows a binomial distribution (BD) 

~ B(n, P) with n as the sample size, and P being the unknown population proportion. Under the null 

hypothesis, the variable B follows ~ B(n, P0 ). 

Note: The sample proportion, p, is computed by comparing the n site observations with the action level, 

A0. Specifically, sample proportion p =B/n = (# of site values in the sample > A0)/n 

STEP 1: Specify a proportion threshold value, P0, and state the following null hypotheses: 

Form 1: H0: P ≤ P0 vs. H1: P > P0 

Form 2: H0: P ≥ P0 vs. H1: P < P0 

Two Sided: H0: P = P0 vs. H1: P ≠ P0 

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STEP 2: Calculate the test statistic: 

p + c P− 0 = (6-2) 0 

0 0 (1 ) 

z 

P P 

n 

−

⎧−0.5

⎪⎪ if , p > P0 x(# of site values > A ) where c = ⎨ n and p = 0 

⎪0.5 if , p < P n 

⎪ n 

0 ⎩

Here c is the continuity correction factor to be able to use normal approximation. 

Use of Large Sample Normal Approximation 

STEP 3: Typically, one should use BD (as described above) to perform this test. However, when both 

(nP0) and n (1- P0) are at least 5, a normal, z-table (automatically computed by ProUCL 4.0) 

approximation may be used to compute the critical values and p-values. 

STEP 4: Conclusion – Given for approximate test based upon normal approximation: 

Form 1: If z0 > z1-α, then reject the null hypothesis that the population proportion, P of exceedances of 

action level, A0 is less than the specified proportion, P0. 

Form 2: If z0 < -z1-α, then reject the null hypothesis that the population proportion, P is more than the 

specified proportion, P0. 

Two Sided: If |z0 | > z1-α/2, then reject the null hypothesis that the population proportion, P is the same as 

the specified proportion, P0. 

P-Values Based Upon a Normal Approximation 

As mentioned before, a p-value is the smallest value for which the null hypothesis is rejected in favor of 

the alternative hypotheses. Thus, based upon the given data, the null hypothesis is rejected for all values 

of α (the level of significance) greater than or equal to the p-value. The details of computing a p-value for 

proportion test based upon large sample normal approximation can be found in any statistical text book 

such as Daniel (1995), and also in EPA (2006). ProUCL 4.0 computes large sample p-values for 

proportion test associated with each form of null hypothesis. If the computed p-value is smaller than the 

specified value of, α, the conclusion is to reject the null hypothesis based upon the collected data set used 

in the various computations. 

Note: ProUCL 4.0 also performs the proportion test based upon the exact binomial distribution when the 

sample size is small, and one may not be able to use the normal approximation as described above. 

ProUCL 4.0 checks for the availability of appropriate amount of data, and performs the tests using a 

normal approximation or the exact binomial distribution accordingly. 

Use of the Exact Binomial Distribution for Smaller Samples 

STEP 1: When the sample size is small (e.g., < 30), either (nP0), or n (1 – P0) is less than 5, one should 

use the exact BD to perform this test. ProUCL 4.0 automatically performs this test based upon BD, when 

the above conditions are not satisfied. In such cases, ProUCL 4.0 computes the critical values and p148 
values based upon the BD and its cumulative distribution function (CDF). The probability statements 

concerning the computation of p-values can be found in Conover (1999). 

STEP 2: Conclusion – Based Upon Exact Binomial Distribution: 

Form 1: Large values of B cause the rejection of the null hypothesis. Therefore, reject the null hypothesis, 

when B≥ b. Here b is obtained using the binomial cumulative probabilities based upon a BD (n, P0). The 

critical value, b (associated with α) is given by the probability statement: Prob(B≥b) = α, or equivalently, 

P(B<b) = (1 – α). As mentioned before, since B is a discrete binomial random variable, the level, α may 

not be exactly achieved by the critical value, b. 

Form 2: For this form, small values of B will cause the rejection of the null hypothesis. Therefore, reject 

the null hypothesis, when B ≤ b. Here b is obtained using the binomial cumulative probabilities based 

upon BD (n, P0). The critical value, b (associated with α) is given by the probability statement: P (B≤b) = 

α. As mentioned before, since B is a discrete binomial random variable, the level, α may not be exactly 

achieved by the critical value, b. 

Two Sided Alternative: Here the critical or the rejection region for the null hypothesis is made of two 

areas, one in the right tail (of area ~ α2) and the other in the left tail (with area ~ α1), so that the combined 

area of the two tails is approximately, α = α1 + α2. That is for this hypothesis form, both small values and 

large values of B will cause the rejection of the null hypothesis. Therefore, reject the null hypothesis, 

when B ≤ b1 or B>b2. Typically α1 and α2 are roughly equal, and in ProUCL 4.0, both are chosen to be 

equal = α /2. Thus, b1 and b2 are given by the statements: P(B≤b1) ~ α/2, and P(B> b2) ~ α/2. Since B is a 

discrete binomial random variable, the level, α may not be exactly achieved by the critical values, b1 and 

b2. 

P-Values Based Upon Binomial Distribution as Incorporated in ProUCL 4.0 

As mentioned before, a p-value is the smallest value for which the null hypothesis is rejected in favor of 

the alternative hypothesis. Thus, based upon the given data, the null hypothesis is rejected for all values of 

α (the level of significance) greater than or equal to the p-value. Note, for discrete distributions such a 

BD, the exact level of significance, α cannot be achieved. The probability statements for computing a pvalue for proportion test based upon BD can be found in Conover (1999). Using the BD, ProUCL 4.0 

computes p-values for proportion test associated with each form of null hypothesis. If the computed pvalue is smaller than the specified value of, α, the conclusion is to reject the null hypothesis based upon 

the collected data set used in the various computations. It is noted that there are some variations in the 

literature about the computation of p-values for proportion test based upon the exact BD. Therefore, the pvalue computation procedure as incorporated in ProUCL 4.0 is described as follows. 

Let b be the calculated value of the binomial random variable, B under the null hypothesis. ProUCL 4.0 

computes the p-values using the following statements: 

Form 1: p-value = Prob (B≥b) 

Form 2: p-value = Prob (B≤b) 

Two sided Alternative: 

 For b> (n-b) 

P-value = 2* Prob (B≤b) 

 For b≤ (n-b) 

149 
P-value = 2*Prob (B≥b) 

Some single sample distribution-free tests based upon the ranks of the data as incorporated in ProUCL 4.0 

are described as follows. These tests can also be used on data sets with NDs, provided the NDs are 

smaller than the cleanup Cs or C. In this Technical Guide, both Cs and C have been used to represent 

cleanup goals or cleanup levels. 

6.6.3 The Sign Test 

The sign test is used to detect a difference between the population median and a fixed cleanup goal, C. 

This test makes no distributional assumptions like the t-test. The sign test is used when the data are not 

symmetric and the sample size is small (EPA, 2006). 

6.6.3.1 Limitations and Robustness 

• This test can handle nondetects, provided all NDs are smaller than the cleanup limit, C. 

• Compared to one-sample t-test, and the Wilcoxon Signed Rank (WSR) test described 

below, the sign test has less power. 

6.6.3.2 Sign Test in the Presence of Nondetects 

• A principal requirement when applying the sign test is that the cleanup limit/goal, C 

should be greater than the greatest less-than value; all NDs should be smaller than C. 

6.6.3.3 Directions for the Sign Test 

Let X1, X2, . . . , Xn represent a random sample of size n collected from a site area under investigation. As 

before, let C represent the cleanup level. In the following, the substantial difference, S is ≥ 0. It should be 

noted that the substantial difference, S ≥ 0 is used only with Form 2 hypothesis with substantial 

difference. 

~ 

STEP 1: Let μ X be the site population median. 

State the following null and the alternative hypotheses: ~ ~ 

Form 1: H0: μ X ≤ C vs. H1: μ X > C 

~ ~ 

Form 2: H0: μ X ≥ C vs. H1: μ X < C 

~ ~ 

Two Sided: H0: μ X = C vs. H1: μ X ≠ C 

~ ~ 

Form 2 with substantial difference, S: Ho: μ X ≥ C+ S vs. H1: μ X < C + S 

STEP 2: Calculate the deviations d = − x C . If some of the d = 0 , then reduce the sample size until all i i i 

the remaining di > 0 . This means that all observations tied at C are ignored from the computation. 

Compute the binomial random variable, B representing the number of di > 0, 

i: = 1,2,...,n. Note that under the null hypothesis, the BD random variable B follows a BD (n, 1⁄2). 

Thus, one can use the exact BD to compute the critical values and p-values associated with this test. 

150 
STEP 3: Use the test statistic, B with exact binomial distribution (BD) for n ≤ 40 (programmed in 

ProUCL 4.0). 

For n > 40 , one may use the approximate normal test statistic given by, 

B − n − S 

z0 = 2 . (6-3) 

n 

4 

Note: As before, the substantial difference, S =0, except for Form 2 hypotheses with substantial 

difference. 

STEP 4: For n ≤ 40 , use the BD table as in EPA, 2006 (critical values automatically computed by 

ProUCL 4.0) to calculate the critical values, and for n > 40 , use the normal approximation and the 

associated normal z critical values. 

STEP 5: Conclusion when n ≤ 40 (following EPA 2006): 

Form 1: If B ≥ BUPPER (n, 2α), then reject the null hypothesis that the population median is less than the 

cleanup level, C. 

Form 2: If B ≤ BUPPER (n, 2α), then reject the null hypothesis that the population median is more than the 

cleanup level. 

Two Sided: If B ≥ BUPPER (n, α) or B ≤ BUPPER (n, α) - 1, then reject the null hypothesis that the 

population median is comparable to the cleanup level, C. 

Form 2 with substantial difference, S: If B ≤ BUPPER (n, 2α), then reject the null hypothesis that the 

population median is more than the cleanup level, C + substantial difference, S. 

Note that ProUCL 4.0 automatically calculates the critical values and p-values based upon the BD (n, 1⁄2) 

for both small samples and large samples. 

Conclusion: When n > 40 – Large Sample Approximation: 

Form 1: If z0 > z1-α, then reject the null hypothesis that the population median is less than the cleanup 

level, C. 

Form 2: If z0 <- z1-α, then reject the null hypothesis that the population median is more than the cleanup 

level, C. 

Two Sided: If |z0 | > z1-α/2, then reject the null hypothesis that the population median is comparable to the 

cleanup level, C. 

Form 2 with substantial difference, S: If z0 <- z1-α, then reject the null hypothesis that the population 

median is more than the cleanup level, C + substantial difference, S. 

P-Values for One-Sample Sign Test 

ProUCL 4.0 automatically calculates the critical values and p-values based upon: the BD (n, 1⁄2) for small 

data sets; and normal approximation for larger data sets as described above. 

151 
6.6.4 The Wilcoxon Signed Rank Test 

The Wilcoxon Signed Rank (WSR) test is used for the testing the difference between the location 

parameter (mean or median) of a population and a fixed cleanup threshold level such as C, Cs also 

representing a location value such as mean concentration. 

6.6.4.1 Limitations and Robustness 

• For symmetric distributions, typically, the Wilcoxon Signed Rank test has more power 

than the sign test. 

• It may give incorrect results in the presence of many tied values. 

• The presence of different detection limits makes this test less powerful. 

• For large samples (n > 50) and the normality assumption of the mean (due to CLT), the 

one-sample t-test is more powerful than the Wilcoxon Signed Rank test. 

6.6.4.2 Wilcoxon Signed Rank (WSR) Test in the Presence of Nondetects 

• When all the data have the same detection limits, replacement of censored data by a 

surrogate value of DL/2 has been recommended (EPA (2006)). 

• In the presence of multiple detection limits, all observations at the highest detection limit 

may be censored (just as in WMW test) – this may result in some loss of power. 

6.6.4.3 Directions for the Wilcoxon Signed Rank Test 

Let X1, X2, . . . , Xn represent the n data points from site under investigation, and C represent the Cleanup 

Level. 

STEP 1: State the following null hypotheses: 

Form 1: H0: Site location ≤ C vs. H1: Site location > C 

Form 2: H0: Site location ≥ C vs. H1: Site location < C 

Two Sided: H0: Site location = C vs. H1: Site location ≠ C 

Form 2 with substantial difference, S: H0: Site location ≥ C + S vs. H1: Site location < C + S, here S ≥ 0. 

STEP 2: Calculate the deviations d = − x C . If d = 0 , then reduce the sample size until d > 0 . i i i i 

STEP 3: Rank the absolute deviations, |di|, from smallest to the largest. Assign an average rank to the 

tied observations. 

STEP 4: Let Ri be the signed rank of |di|, where the sign of Ri is determined by the sign of di. 

STEP 5: Test Statistic Calculations: 

+ For n ≤ 20, compute T = ∑ Ri , where T + is the sum of the positive signed ranks. 

{ :i Ri >0} 

For n > 20, approximate test is given by 

152 
T − ( + ) + n n 1 / 4 

z0 = (6-4) + var (T )

nn g 

where var (T +

) is given by var (T +) = ( +1 () 2 n +1) 

− 1 ∑tj (tj 

2 −1) with g = number of tied 

24 48 j=1 

groups. 

STEP 6: Conclusion when n ≤ 20: 

n n( +1) Form 1: If T + ≥ , then reject the null hypothesis that the location parameter is less than the 2 

cleanup level, C. 

Form 2: IfT + ≤ wα , then reject the null hypothesis that the location parameter is more than the cleanup 

level, C. 

Two Sided: If T + ≥

n n( +1) − wα /2 orT + ≤ wα , then reject the null hypothesis that the location 2 

parameter is comparable to the action level, C. 

Form 2 with substantial difference, S: IfT + ≤ wα , then reject the null hypothesis that the location 

parameter is more than the cleanup level, C + the substantial difference, S. 

Conclusion when n > 20: 

Form 1: If z0 > z1-α, then reject the null hypothesis that the location parameter is less than the cleanup 

level, C. 

Form 2: If z0 < - z1-α, then reject the null hypothesis that the location parameter is more than the cleanup 

level, C. 

Two Sided: If |z0 | > z1-α/2, then reject the null hypothesis that the location parameter is comparable to the 

cleanup level, C. 

Form 2 with substantial difference, S: If z0 < - z1-α, then reject the null hypothesis that the location 

parameter is more than the cleanup level, C + the substantial difference, S. 

Note: The critical values, wα as given in EPA (2006) have been programmed in ProUCL 4.0. The details 

of computation of p-values for small samples (based upon a BD) and larges samples (based upon a 

normal distribution) as incorporated in ProUCL 4.0 are given in EPA (2006). For small data sets, 

ProUCL 4.0 used tables of critical values as given in EPA (2006). 

6.7 Two-Sample Hypotheses Testing Approaches 

The use of parametric and nonparametric two-sample hypotheses testing approaches is quite common in 

many environmental applications including site versus background comparison studies. Several of those 

approaches for data sets with and without ND observations have been incorporated in ProUCL 4.0. 

Additionally some graphical methods (box plots and Q-Q plots) for data sets with and without NDs are 

also available in ProUCL 4.0 to visually compare two or more populations. Some of the parametric and 

nonparametric methods are described in the following subsections. Additional details can be found in 

EPA (1994, 1997, and 2006). 

153 
Note: It is suggested to always supplement the conclusions derived using test statistics (e.g., t-test, WMW 

test, Gehan test) with graphical displays such as multiple Q-Q plots and side-by-side box plots. Graphical 

displays of data sets often provide useful information about the behavior of the populations (and their 

parameters) under investigation that may not be obtained and understood by simple test statistics such as 

t-test, Gehan test, and even GOF test statistics. Therefore, one should always use graphical displays 

before deriving any conclusions about the data distributions, or equality of two data distributions. 

Student’s two-sample t-test is used to compare the means of the two populations such as the potentially 

impacted site area and a background or reference area. Two cases arise: 1) the variations (dispersion) of 

the two populations are the same, and 2) the dispersions of the two populations are not the same. 

Generally, a t-test is robust and not sensitive to small deviations from the assumptions of normality. 

6.7.1 Student’s Two-Sample t-Test (Equal Variances) 

6.7.1.1 Assumptions and Their Verification 

• X1, X2, ..., Xn represent systematic site samples and Y1, Y2, ... , Ym represent systematic 

background samples that are drawn at random from those independent populations. The 

validity of the random sampling and independence assumptions should be confirmed by 

reviewing the procedures used to select the sampling points (EPA, 2006). 

• The sample means X (site) and Y (background) are approximately normally distributed 

provided the underlying data distributions are not heavily skewed (Singh, Singh, and Iaci, 

2002). If both m and n are large (and data are mildly to moderately skewed), one may 

make this assumption without further verification. If the data are heavily skewed 

(skewness discussed in Chapters 3 and 4), the use of nonparametric tests such as WMW 

test and quantile test is preferable. Normality or approximate normality of data sets 

should be checked by using GOF tests as incorporated in ProUCL 4.0 and described in 

Chapter 3 of this Technical Guide. 

6.7.1.2 Limitations and Robustness 

• The two-sample t-test with equal variances is robust to violations of the assumption of 

normality. However, if the investigator has tested and rejected normality or equality of 

variances, then nonparametric procedures such as the Wilcoxon-Mann-Whitney (WMW) 

may be applied. 

• This test is not robust to outliers because sample means and standard deviations are 

sensitive to outliers. As mentioned before, it is suggested not to use a t-test on logtransformed data sets. This issue has been discussed in Chapter 3 of the revised Guidance 

for Comparing Background and Chemical Concentrations in Soil for CERCLA Sites 

(EPA, 2002b). 

• The test assumes the equality of variances of the background and the potentially impacted 

data sets. Therefore, if the two variances are not equal and normality assumptions of the 

means are valid, then Satterthwaite’s t-test (available in ProUCL 4.0) should be applied. 

However, if the variances are not equal and the normality of the means is not applicable 

(due to small samples, or skewed data), then nonparametric WMW test should be applied. 

154 
• In the presence of less than values, it is suggested to use WMW test or Gehan test. 

Sometimes, users tend to use a t-test on data sets obtained by replacing all less than 

values by surrogate values, such as respective DL/2 values, or DL values. 

• Note: As mentioned many times before, it is suggested to avoid the use of such proxy and 

substitution methods (e.g., DL/2) to compute t-test statistics. 

6.7.1.3 Guidance on Implementing the Student’s Two-Sample t-Test 

The number of site (n) and background (m) measurements required to conduct the two-sample t-test 

should be calculated based upon appropriate DQO procedures (EPA, 2006). In case, it is not possible to 

use DQOs, or to collect as many samples as determined using DQO-based sample size determination 

formulae, one should follow the minimum sample size requirements as described in Chapter 1 of this 

Technical Guide. 

ProUCL 4.0 provides a test to verify the equality of two dispersions. ProUCL 4.0 automatically performs 

this test for dispersions before using one of the two t-tests incorporated in ProUCL 4.0. If some 

measurements appear to be unusually large compared to the remainder of the measurements in the data 

set, then a test for outliers (Chapter 7) should be conducted. Some of the identified outliers may represent 

contaminated locations needing further investigation. Once any identified outliers have been investigated 

for being mistakes or errors and, if necessary, discarded, the site and background data sets should be retested for normality using both probability plots and formal GOF tests. 

The project team should decide the proper disposition of outliers. In practice, it is advantageous to carry 

out the tests on data sets with and without the outliers. This extra step helps the users to assess and 

determine the influence of outliers on the various test statistics and the resulting conclusions. This process 

also helps the users in making appropriate decisions about the proper disposition (include or exclude from 

the data analyses) of outliers. Many times, the outliers represent contaminated site locations requiring 

separate and additional investigation. 

6.7.1.4 Directions for the Student’s Two-Sample t-Test 

Let X1, X2, . . . , Xn represent systematic and random site samples and Y1, Y2, . . . , Ym represent systematic 

and random background samples drawn from independent populations. 

STEP 1: State the following null and the alternative hypotheses: 

Form 1: H0: μ X − μY ≤ 0 vs. H1: μ X − μY > 0 

Form 2: H0: μ X − μY ≥ 0 vs. H1: μ X − μY < 0 

Two Sided: H0: μ X − μY = 0 vs. H1: μ X − μY ≠ 0 

Form 2 with substantial difference, S: H0: μ X − μY ≥ S vs. H1: μ X − μY < S 

STEP 2: Calculate the sample mean X and the sample variances S X

2 for the site data and compute the 

sample mean Y and the sample variance SY

2 for the background data. 

155 
STEP 3: Determine if the variances of the two populations are equal. If the variances of the two 

populations are not equal, use the Satterthwaite’s t-test. Calculate, 

(n −1) sx 

2 (m 1) 2 + − y s 

sp = (m − + 1) (n −1) (6-5) 

− − and t 0 = (X Y ) S

(6-6) 1 1 

sp +

mn 

STEP 4: Use ProUCL 4.0 to find the critical value t1-α such that 100(1 – α) % of the t-distribution with (m 

+ n - 2) degrees of freedom (df) is below t1-α. 

STEP 5: Conclusion: 

Form 1: If t0 > tm+n-2, 1-α, then reject the null hypothesis that the site population mean is less than or equal 

to the background population mean. 

Form 2: If t0 < -tm+n-2, 1-α, then reject the null hypothesis that the site population mean is greater than or 

equal to the background population mean. 

Two Sided: If |t0 | > tm+n-2, 1-α/2, then reject the null hypothesis that the site population mean comparable to 

the background population mean. 

Form 2 with substantial difference, S: If t0 <- tm+n-2, 1- α, then reject the null hypothesis that the site 

population mean is greater than or equal to the background population mean + the substantial difference, 

S. 

6.7.2 The Satterthwaite Two-Sample t-Test (Unequal Variances) 

Satterthwaite’s t-test should be used to compare two population means when the variances of the two 

populations are not equal. It requires the same assumptions as the two-sample t-test (described above) 

except for the assumption of equal variance. 

6.7.2.1 Limitations and Robustness 

• In the presence of less than values, replacement by a surrogate value such as the detection 

limit or one-half of the detection limit gives biased results. Its use should be avoided. 

Instead the use of nonparametric tests is suggested. 

• In cases where the assumptions of normality of means are violated, the use of 

nonparametric tests is preferred. 

• Wilcoxon-Mann-Whitney (WMW) and quantile tests are recommended when data sets 

have less than values. Moreover, if the less-than values have multiple detection limits (for 

example, < 10, < 15, etc.), then the Gehan test should be used in place of the WMW test. 

156 
6.7.2.2 Directions for the Satterthwaite Two-Sample t-Test 

Let X1, X2, . . . , Xn represent systematic and random site samples and Y1, Y2, . . . , Ym represent systematic 

and random background samples drawn from independent populations. 

STEP 1: State the following null and the alternative hypotheses: 

Form 1: H0: μ X − μY ≤ 0 vs. H1: μ X − μY > 0 

Form 2: H0: μ X − μY ≥ 0 vs. H1: μ X − μY < 0 

Two Sided: H0: μ X − μY = 0 vs. H1: μ X − μY ≠ 0 

Form 2 with substantial difference, S: H0: μ X − μY ≥ S vs. H1: μ X − μY < S 

STEP 2: Calculate the sample mean X and the sample variances S X

2 for the site data and compute the 

sample mean Y and the sample variance SY

2 for the background data. 

STEP 3: Use F test as described below (also in ProUCL 4.0) to determine if the variances of the two 

populations are equal. 

X −Y − S

t0 = (6-7) 2 2

sX sY +

n m 

Here S = 0, except when used in Form 2 hypothesis with substantial difference, S ≥ 0. 

STEP 4: Use a t-table (ProUCL 4.0 computes it) to find the critical value t1-α such that 100(1 – α)% of the 

t-distribution with f degrees of freedom is below t1-α, where the Satterthwaite’s Approximation for df is 

given by: 

⎡sX 

2 

+

sY 

2 ⎤

2 

⎢ ⎥

df = 4 

⎣ n m ⎦

4 (6-8)

sX +sY 

2 2

nn ( −1) mm ( −1) 

STEP 5: Conclusion: 

Form 1: If t0 > tdf, 1-α, then reject the null hypothesis that the site population mean is less than or equal to 

the background population mean. 

Form 2: If t0 < -tdf, 1-α, then reject the null hypothesis that the site population mean is greater than or equal 

to the background population mean. 

Two Sided: If |t0 | > tdf, 1-α/2, then reject the null hypothesis that the site population mean is comparable to 

the background population mean. 

157 
Form 2 with substantial difference, S: If t0 < -tdf, 1- α, then reject the null hypothesis that the site population 

mean is greater than or equal to the background population mean + the substantial difference, S. 

P-Values for Two-Sample t-Test 

A p-value is the smallest value for which the null hypothesis is rejected in favor of the alternative 

hypotheses. Thus, based upon the given data, the null hypothesis is rejected for all values of α (the level 

of significance) greater than or equal to the p-value. The details of computing a p-value for two-sample ttest for comparing two means are given in EPA (2006). ProUCL 4.0 directly computes (based upon an 

appropriate t-distribution) p-values for two-sample t-tests associated with each form of null hypothesis. If 

the computed p-value is smaller than the specified value of, α, the conclusion is to reject the null 

hypothesis based upon the collected data set used in the various computations. 

6.8 Tests for Equality of Dispersions 

This section describes a test (also available in ProUCL 4.0) that verifies the assumption of the equality of 

two variances. This assumption is needed to perform a simple two-sample Student’s t-test as described 

above. 

6.8.1 The F-Test for the Equality of Two-Variance 

An F-test may be used to test whether the true underlying variances of two populations are equal. Usually 

the F-test is employed as a preliminary test, before conducting the two-sample t-test for the equality of 

two means. The assumptions underlying the F-test are that the two-samples are independent random 

samples from two underlying normal populations. The F-test for equality of variances is highly sensitive 

to departures from normality. 

6.8.1.1 Directions for the F-Test 

Let X1, X2, . . . , Xn represent the n data points from site and Y1, Y2, . . . , Ym represent the m data points 

from background. To perform an F-test, proceed as follows. 

2 2 

STEP 1: Calculate the sample variances s X (for the X’s) and sY (for the Y’s) 

2 2 2 2

STEP 2: Calculate the variance ratios FX = sX /sY and FY = sY /sX . Let F equal the larger of these two 

values. If F = Fx, then let k = n - 1 and q = m - 1. If F = Fy, then let k = m - 1 and q = n – 1. 

STEP 3: Using a table of F distribution (ProUCL 4.0 computes them), find a cutoff, U = f1-α/2(k, q) 

associated with F distribution with k and q degrees of freedom for some significance level, α. If F 

calculated as above > U, conclude that the variances of the two populations are not the same. 

P-Values for Two-Sample Dispersion Test for Equality of Variances 

As mentioned before, a p-value is the smallest value for which the null hypothesis is rejected in favor of 

the alternative hypotheses. Thus, based upon the given data, the null hypothesis is rejected for all values 

of α (the level of significance) greater than or equal to the p-value. The details of computing a p-value for 

a two-sample F-test to compare equality of two variances (of two normal populations) are given in EPA 

(2006). ProUCL 4.0 computes p-values for the two-sample F-test based upon an appropriate F158 
distribution. If the computed p-value is smaller than the specified value of, α, the conclusion is to reject 

the null hypothesis based upon the collected data set used in the various computations. 

6.9 Nonparametric Tests 

The statistical tests discussed in the previous section rely on the assumption of normality of the population (e.g., background and site) distributions. When the data do not follow the assumed distribution, use 

of parametric statistical tests may lead to inaccurate conclusions. Additionally, if the data sets contain 

outliers or nondetect values, an additional level of uncertainty is faced when conducting parametric tests. 

Since most environmental data sets do contain outliers and nondetect values, it is unlikely that the current 

widespread use of parametric tests is justified, given that these tests may be adversely affected by outliers 

and by assumptions made for handling nondetect values. Several nonparametric tests have been 

incorporated in ProUCL 4.0 that can be used on data sets with ND observations with single and multiple 

detection limits. 

As mentioned earlier, tests that do not require specific mathematical form for the underlying distribution 

are called distribution-free or nonparametric statistical tests. The property of robustness is the main 

advantage of nonparametric statistical tests. Nonparametric tests have good test performances for a wide 

variety of distributions, and those performances are not unduly affected by outliers. This is especially true 

when the statistics and tests are not based upon higher order statistics such as nonparametric UPLs and 

UTLs. An example illustrating these issues has been considered in Chapter 5. It was shown that the 

nonparametric background statistics do get distorted by outliers. This statement is true for data sets with 

or without ND observations. 

Nonparametric tests can be used on normal as well as for non-normal data sets. If one or both of the data 

sets fail to meet the normality test, or if the data sets appear to come from different types of populations, 

then the use of nonparametric tests is preferable. It should be noted that parametric tests are more 

powerful provided the underlying assumptions associated with those tests are satisfied. Nonparametric 

tests compare the shape and location of the two distributions instead of a statistical parameter such as 

mean. Nonparametric tests are preferred methods whenever data set consists of ND observations, 

especially when the percentage of nondetects becomes very high. 

Note: Once again, it is suggested to avoid the use of DL/2 method (e.g., replacing NDs by respective 

DL/2 values or some other substitution values) to perform hypotheses testing approaches. ProUCL 4.0 

provides alternative methods (e.g., WMW test, Gehan test) that may be used when data sets consist of ND 

observations. 

6.9.1 The Wilcoxon-Mann-Whitney (WMW) Test 

The Mann-Whitney (or Wilcoxon-Mann-Whitney) test (Bain and Engelhardt, 1991) is a nonparametric 

test used for determining whether a difference exists between the site and the background population 

distributions. The WMW test tests whether or not measurements (location, central) from one population 

consistently tend to be larger (or smaller) than those from the other population based upon the assumption 

that the dispersion of the two distributions are roughly the same. This test determines which distribution is 

higher by comparing the relative ranks of the two data sets when the data from both sources are sorted 

into a single list. One assumes that any difference between the background and site concentration distributions is due to a shift in location (mean, median) of the site concentrations to higher values (due to the 

presence of contamination in addition to the background). 

159 
6.9.1.1 Advantages and Disadvantages 

The WMW test has three advantages for background comparisons: 

• The two data sets are not required to be from a known type of distribution. The WMW 

test does not assume that the data are normally distributed, although a normal distribution 

approximation is used to determine the critical value of the test for large sample sizes. 

Note: It is suggested again to avoid the use of t-test on log-transformed data sets to compare the 

mean concentrations of two populations. It should be noted that the equality of means in log-scale 

does not imply the equality of means in the original raw scale. The cleanup decisions often are made 

based upon concentrations in threshold levels in the original scale. 

• WMW tests allows for nondetect measurements to be present in both data sets. 

Specifically, the WMW test can handle a moderate number of nondetect values in either 

or both data sets by treating them as ties. In practice, the WMW test may be used with up 

to 40 percent or more nondetect measurements in either the background or the site data. If 

more than 40 percent of the data from either the background or the site are nondetect 

values, the WMW test should not be used. The use of Gehan test is preferable in those 

situations. 

• It is robust with respect to outliers because the analysis is conducted in terms of the ranks 

of the data. This limits the influence of outliers because a given data point can be no more 

extreme than the first or the last rank. 

• The WMW test should not be used if more than 40% of the site or background data sets 

are less-than values. The measurement laboratories should be instructed to report actual 

measurements for all soil samples, whenever possible, even if the reported measurements 

are negative. Although negative concentrations cannot occur in nature, negative 

measurements can occur, due to measurement uncertainties, when the true concentrations 

are very close to zero. 

• The WMW test does not place enough weight on the larger site and background 

measurements. This means, a WMW may lead to the conclusion that two populations are 

comparable even when the observations in the right tail of one distribution (e.g., site) are 

significantly larger than the right tail observations of other population (e.g., background). 

The quantile test (EPA, 1994) is used to compare upper tails of the two distributions. 

WMW test uses and considers all measurements, rather than focusing on larger 

measurements as is done by the quantile test. The quantile test is available in ProUCL 

4.0. It is suggested that both the quantile test and the WMW test be used on the same data 

sets. 

• The WMW test may be applied to either null hypothesis in the two forms of background 

tests as discussed throughout this chapter. In all forms, the null hypothesis is assumed to 

be true unless the evidence in the data indicates that it should be rejected in favor of the 

alternative hypothesis. 

160 
6.9.1.2 WMW Test in the Presence of Nondetects 

• If there are t nondetect values, then they are considered as “ties” and are assigned the 

t +1

average rank for this group. Their average rank is the average of the first t integers, . 2 

• Note that if there are no NDs, the process is the same and ranks are assigned individually 

to all detected observations. 

• If more than one detection limit was in use, then all of the observations below the largest 

detection limit should be treated as nondetects. This of course will result in some loss of 

power associated with WMW test. Alternatively, the Gehan test may be performed. 

6.9.1.3 WMW Test Assumptions and Their Verification 

The underlying assumptions of the WMW test are: 

• The soil sample measurements obtained from the site and background areas are 

independent (not correlated). This assumption requires: 1) that an appropriate probabilitybased sampling design strategy be used to determine (identify) the sampling locations of 

the soil samples for collection, and 2) those soil sampling locations are spaced far enough 

apart that a spatial correlation among concentrations at different locations is not likely to 

be present. 

• The underlying probability distribution of the measurements from a site area is similar to 

(e.g., including shape and spread) the probability distribution of measurements collected 

from a background or reference area. Under the alternative hypothesis (Form 2), the 

distribution of site data may be shifted to higher concentrations than the distribution for 

the background area. The assumption of equal variances of the two regions (site and 

background) should also be evaluated using descriptive statistics and graphical displays 

such as side-by-side box plots and histograms. 

6.9.1.4 Directions for the WMW Test when the Number of Site and Background Measurements 

is small (n # 20 and m # 20) 

Let X1, X2, . . . , Xn represent the n data points from the site population and Y1, Y2, . . . , Ym represent the 

m data points from the background population. 

~ ~ 

STEP 1: Let μ X represent the site population median and μY represent the background population 

median. State the following null and the alternative hypotheses: 

~ ~ ~ ~ 

Form 1: H0: μ X − μY ≤ 0 vs. H1: μ X − μY > 0 

~ ~ ~ ~ 

Form 2: H0: μ X − μY ≥ 0 vs. H1: μ X − μY < 0 

~ ~ ~ ~ 

Two Sided: H0: μ X − μY = 0 vs. H1: μ X − μY ≠ 0 

~ ~ ~ ~ 

Form 2 with substantial difference, S: H0: μ X − μY ≥ S μ X − μY < S 

161 
STEP 2: List and rank the pooled set of N = n + m site and background measurements from smallest to 

largest, keeping track of which measurements came from the site and which came from the background 

area. Assign the rank of 1 to the smallest value among the pooled data, the rank of 2 to the second 

smallest value among the pooled data, and so forth. 

• If a few measurements are tied (identical in value), then assign the average of the ranks 

that would otherwise be assigned to the tied observations. If several measurement values 

have ties, then average the ranks separately for each of those measurement values. 

• If a few less-than values occur (say, < 10%), and if all such values are less than the 

smallest detected measurement in the pooled data set, then handle the less-than values as 

tied at an arbitrary value less than the smallest detected measurement. Assign the average 

of the ranks that would otherwise be assigned to these tied less-than values (the same 

procedure as for tied detected measurements). 

• If between 10% and 40% of the pooled data set are less-than values, and all are less than 

the smallest detected measurement, then use the approximate WMW test procedure given 

for large data sets (e.g., n > 20 and m > 20), even if n and m are less than 20. 

Note: The procedure is for the case where m and n are both of size 20 or larger. That procedure will 

provide only an approximate test if it is used when n and m are both smaller than 20. In that case, 

decisions of whether or not the null hypothesis is rejected should not be made until additional information 

is obtained by taking more samples and using a more sensitive measurement method. It is suggested to 

use graphical displays before deriving any conclusions about the equality of two data distributions. 

STEP 3: Calculate the sum of the ranks of the site measurements. Denote this sum by Ws and then 

calculate T as follows: 

nn ( +1) T W= S − − S (6-9) 2 

Note: The test statistic, T is often called the MW test statistic. In this Guide and in ProUCL, this statistic 

is called the WMW test statistic (Bain and Engelhardt, 1991). Note the difference between the definitions 

of T and Ws . Some software packages such as MINITAB uses test statistic Ws (known as Wilcoxon Rank 

Sum (WRS) statistic) to perform this nonparametric test. Obviously the critical values for Ws and T are 

different. However, critical values for one test can be obtained from the critical values of the other test by 

using the relationship given by the above equation. These two tests (WRS test and WMW test) are 

equivalent tests, and conclusions derived by using these test statistics are equivalent. 

STEP 4: For specific values of n, m, and α, find an appropriate Wilcoxon-Mann-Whitney critical value,

wα, from the table as given in EPA (2006) and also in Daniel (1995). 

STEP 5: Conclusion: 

Form 1: If T ≥ nm - wα, then reject the null hypothesis that the site population median is less than or equal 

to the background population median. 

Form 2: If T ≤ wα, then reject the null hypothesis that the site population median is greater than or equal to

the background population median. 

162 
Two Sided: If T ≥ nm - wα/2 or T≤ wα/2, then reject the null hypothesis that the site population median 

(location) is similar to that of the background population median (location). 

Form 2 with substantial difference, S: If T ≤ wα, then reject the null hypothesis that the site population 

median is greater than or equal to the background population median + the substantial difference, S. 

Note: S takes a positive value only for this form of the hypothesis with substantial difference, in all other 

forms of the null hypothesis, S = 0. 

P-Values for Two-Sample WMW Test – For Small Sample 

A p-value is the smallest value for which the null hypothesis is rejected in favor of the alternative 

hypotheses. Thus, based upon the given data, the null hypothesis is rejected for all values of α (the level 

of significance) greater than or equal to the p-value. The details of computing a p-value for a two-sample 

nonparametric WMW test for comparing two means are given in EPA (2006). For small samples, 

ProUCL 4.0 computes only approximate (as computed for large samples) p-values for WMW test. If the 

computed p-value is smaller than the specified value of, α, the conclusion is to reject the null hypothesis 

based upon the collected data set used in the various computations. 

6.9.1.5 Directions for the WMW Test when the Number of Site and Background Measurements 

is Large (n > 20 and m > 20) 

Let X1, X2, . . . , Xn represent the n data points from the site population and Y1, Y2, . . . , Ym represent the m 

data points from the background population. ~ ~ 

STEP 1: Let μ X represent the site and μY represent the background population medians (means). State 

the following null and the alternative hypotheses: 

~ ~ ~ ~ 

Form 1: H0: μ X − μY ≤ 0 vs. H1: μ X − μY > 0 

~ ~ ~ ~ 

Form 2: H0: μ X − μY ≥ 0 vs. H1: μ X − μY < 0 

~ ~ ~ ~ 

Two Sided: H0: μ X − μY = 0 vs. H1: μ X − μY ≠ 0 

~ ~ ~ ~ 

Form 2 with substantial difference, S: H0: μ X − μY ≥ S vs. μ X − μY < S 

STEP 2: List and rank the pooled set of n + m site and background measurements from smallest to 

largest, keeping track of which measurements came from the site and which came from the background 

area. Assign the rank of 1 to the smallest value among the pooled data, the rank of 2 to the second 

smallest value among the pooled data, and so forth. 

• If < 40% of the measurements in the pooled data is tied (identical in value), then assign 

the average of the ranks that would otherwise be assigned to the tied observations. If 

several measurement values exist for which ties occur, then average the ranks separately 

for each of those measurement values. 

• If < 40% of the pooled data is less-than values and if all such values are less than the 

smallest detected measurement in the pooled data set, then handle those less-than values 

as being tied at an arbitrary value less than the smallest detected measurement. Assign the 

163 
average of the ranks that would otherwise be assigned to this group of tied values (the 

same procedure as for detected measurements that are tied). 

• NOTE: The total number of tied detected measurements and tied less-than values should 

not exceed 40% of the total number of measurements. 

• If more than 40% of the pooled data are less-than values, then the Gehan test should be 

used. 

STEP 3: Calculate the sum of the ranks of the site measurements. Denote this sum by Ws. 

STEP 4: Calculate 

W − ( + m +1) 

− S nn 

Z0 = s 2 (6-10)

nm ( n + + m 1) 

12 

STEP 5: Use the z-table to get the critical values z1-α , where z1-α is the 100(1 – α) percentile of the 

standard normal distribution. 

STEP 6: Conclusion for Large Sample Approximations: 

Form 1: If Z0 > z1-α, then reject the null hypothesis that the site population mean/median is less than or 

equal to the background population mean/median. 

Form 2: If Z0 < - z1-α, then reject the null hypothesis that the site population mean is greater than or equal 

to the background population mean. 

Two Sided: If |Z0| > z1-α/2, then reject the null hypothesis that the site population mean is same as the 

background population mean. 

Form 2 with substantial difference, S: If Z0 < - z1-α, then reject the null hypothesis that the site population 

mean is greater than or equal to the background population location + the substantial difference, S. 

P-Values for Two-Sample WMW Test – For Large Sample 

A p-value is the smallest value for which the null hypothesis is rejected in favor of the alternative 

hypotheses. Thus, based upon the given data, the null hypothesis is rejected for all values of α (the level 

of significance) greater than or equal to the p-value. The details of computing a large sample p-value for a 

two-sample nonparametric WMW test for comparing two means/medians are given in EPA (2006). 

ProUCL 4.0 directly computes (based upon normal distribution) p-values large samples WMW test for 

each form of null hypothesis. If the computed p-value is smaller than the specified value of, α, the 

conclusion is to reject the null hypothesis based upon the collected data set used in the various 

computations. 

Note: As suggested in the literature (EPA, 1994), both the WMW test and the quantile test should be used 

on the same data set to compare the two data distributions. Typically, the WMW test is used to compare 

the measures of locations (central tendencies assuming the equality of dispersions and shape of the 

distributions), whereas a quantile test is useful to determine if the observations in the upper tail of the site 

data set are larger than those found in the upper tail of the background data set. A typical WMW test 

simply compares the measures of location (central tendencies) of the two distributions (populations). A 

164 
WMW test cannot compare the upper tails of two data distributions. Specifically, a WMW test may lead to 

the conclusion of the similarity/equality of the two data distributions even when the upper tail of one data 

distribution is much larger (shifted to the right) than the upper tail of the other data distribution. In order 

to detect the differences and shifts in the tails of two data distributions, one should use quantile test 

described as follows. 

6.9.2 The Gehan Test 

The Gehan test is one of several nonparametric tests that has been proposed to test for the differences 

between two sites when the data sets have multiple censoring points and detection limits. Among these 

tests, Palachek et al. (1993) indicate they selected the Gehan test primarily because it was the easiest to 

explain, because the several methods generally behave comparably, and because the Gehan test reduces to 

the WRS test, a relatively well-known test to environmental professionals. Palachek et al. (1993) used 

their computer code to conduct Gehan tests on data from the Rocky Flats Environmental Technology Site 

near Denver, CO. They recommend using the Gehan test rather than a more complicated procedure 

involving replacement of nondetects by a value such as one-half of the detection limit, testing for 

distribution shape and variance, and then conducting appropriate t- tests or the WMW test. The Gehan test 

as described here is available in ProUCL 4.0. 

6.9.2.1 Limitations and Robustness 

The Gehan test can be used when the background or site data sets contain multiple less-than values with 

different detection limits. 

• The Gehan test is somewhat tedious to compute by hand. The use of a compute program 

is desirable. 

• If the censoring mechanisms are different for the site and background data sets, then the 

test results may be an indication of this difference in censoring mechanisms rather than 

an indication that the null hypothesis is rejected. 

Note: The Gehan test is used when many ND observations or multiple DLs may be present in the two data 

sets. Therefore, the conclusions derived using this test may not be reliable when dealing with samples of 

sizes smaller than 10. Furthermore, it has been suggested throughout this guide to have a minimum of 8­

10 observations (from each of the population) to use hypotheses testing approaches, as decisions derived 

based upon smaller data sets may not be reliable enough to draw important decisions about the human 

health and the environment. Therefore, this test (as included in ProUCL 4.0) is described here for data 

sets of sizes ≥ 10. The test described as follows is based upon the normal approximation of Gehan’s 

statistic. 

6.9.2.2 Directions for the Gehan Test when m ≥ 10 and n ≥ 10. 

Let X1, X2, . . . , Xn represent data points from the site population Y1, Y2, . . . , Ym represent background data 

from the background population. For data sets of sizes greater than or equal to 10, a test based upon 

normal approximations is described in the following. 

~ ~ 

STEP 1: Let μ X represent the site and μY represent the background population medians. State the 

following null and the alternative hypotheses: 

165 
~ ~ ~ ~ 

Form 1: H0: μ X − μY ≤ 0 vs. H1: μ X − μY > 0 

~ ~ ~ ~ 

Form 2: H0: μ X − μY ≥ 0 vs. H1: μ X − μY < 0 

~ ~ ~ ~ 

Two Sided: H0: μ X − μY = 0 vs. H1: μ X − μY ≠ 0 

~ ~ ~ ~ 

Form 2 with substantial difference, S: H0: μ X − μY ≥ S vs. H1: μ X − μY < S 

STEP 2: List the combined m background and n site measurements, including the less-than values, from 

smallest to largest, where the total number of combined samples is N = m + n. The less-than symbol (<) 

is ignored when listing the N data from smallest to largest. 

STEP 3: Determine the N ranks, R1, R2, ... , Rn, for the N ordered data values using the method described 

in the example given below. 

STEP 4: Compute the N scores, a(R1), a(R2),..., a(Rn), using the formula a(Ri) = 2Ri – N -1, where i is 

successively set equal to 1, 2, ..., N. 

STEP 5: Compute the Gehan statistic, G, as follows: 

N 

∑ha ( ) R i i 

G = i=1 

1 (6-11) 

N [( i )] 2 ⎤ 2 ⎡ aR 

⎣ i=1 ( ⎢mn∑ NN −1) ⎦

⎥

⎧hi =1 

where ⎨

⎩hi = 0 

hi = 1 if the i

th datum is from the site population 

hi = 0 if the i

th datum is from the background population 

N = n + m 

a(Ri) = 2 Ri – N -1, as indicated above. 

STEP 6: Use the normal z-table to get the critical values. 

STEP 7: Conclusion based upon approximate normal distribution of G statistic: 

Form 1: If G ≥ z1-α, then reject the null hypothesis that the site population median is less than or equal to 

the background population median. 

Form 2: If G ≤- z1-α, then reject the null hypothesis that the site population median is greater than or equal 

to the background population median. 

Two Sided: If |G| ≥ z1-α/2, then reject the null hypothesis that the site population median is same as the 

background population median. 

166 
Form 2 with substantial difference, S: If G ≤ - z1-α, then reject the null hypothesis that the site population 

median is greater than or equal to the background population median and the substantial difference. 

P-Values for Two-Sample Gehan Test 

A p-value is the smallest value for which the null hypothesis is rejected in favor of the alternative 

hypotheses. Thus, based upon the given data, the null hypothesis is rejected for all values of α, the level of 

significance, greater than or equal to the p-value. For Gehan’s test as described above, the p-values are 

computed using normal approximation for Gehan’s G statistic. The p-values can be computed using the 

simple procedure as used for computing large sample p-values for a two-sample nonparametric WMW 

test. ProUCL 4.0 directly computes (based upon normal distribution) p-values for Gehan test for each 

form of null hypothesis. If the computed p-value is smaller than the specified value of, α, the conclusion 

is to reject the null hypothesis based upon the collected data set used in the various computations. 

6.9.3 The Quantile Test 

The quantile test (EPA, 1994) is a nonparametric test and is useful to detect a shift to the right in the righttails of the site and background distributions. The quantile test when used in parallel with WMW test 

provides the user with stronger evidence to make decisions on whether the site has attained remediation 

(or background) levels or not (or if site and background concentrations are comparable). It is noted that 

the critical values for the quantile test are available for Background Form 1 hypothesis. Therefore, for 

quantile test, only Form 1 of hypothesis testing is available in ProUCL 4.0. Specifically, the null 

hypothesis is: Concentration in the cleanup unit (AOC) is comparable to that of the background area. A 

partial formulation of the quantile test (as given in EPA, 1994) is described as follows. 

6.9.3.1 Limitations and Robustness 

• It may give unreliable results if less than values are present in the largest detected 

observations. 

• If the quantile test does not declare that the chemical is a COPC, then a Wilcoxon-MannWhitney test should be performed to ascertain the results. 

• Since the test focuses on the right tails, presence of large outliers will bias results. 

• It does not require any distributional assumption. 

• As the test focuses on the right tail of the site and background distributions, it can have 

more power to detect differences than the Gehan, Wilcoxon-Mann-Whitney or the twosample t-tests. 

• Relatively simple to conduct. 

6.9.3.2 Quantile Test in the Presence of Nondetects 

• A principal requirement when applying the quantile test on censored data sets is to 

discard all less-than values present in the largest r detected observations. 

6.9.3.3 Directions for the Quantile Test 

Let X1, X2, . . . , Xn represent the n data points from the site population and Y1, Y2, . . . , Ym represent the 

m data points from the background population. 

STEP 1: State the following null and the alternative hypotheses: 

167 
~ ~ ~ ~ 

Form 1: H0: μ X − μY ≤ 0 vs. H1: μ X − μY > 0 

STEP 2: From the tables (EPA (1994)) look up the values of r, k, and α corresponding to m and n. The 

formula for computing the actual α is given below. 

r ⎛m n + − r ⎞⎛r⎞ ∑⎜ ⎟⎜ ⎟ = ni ⎠ i

α = ik ⎝ − ⎝ ⎠ (6-12) ⎛m n + ⎞ ⎜ ⎟ ⎝ n ⎠

STEP 3: If the actual α and the desired α (0.05, 0.01, etc.) do not agree, then increase or decrease r or k 

by 1 unit and again compute the actual α. Use the new r and k value in STEP 3. 

Note: As r and k are discrete in nature, getting the same value of α from the formula and from the table 

may not be achievable. 

STEP 4: Order the pooled data set from smallest to largest. In the presence of ties, increase r to include 

all ties in the r largest observations; e.g., when r = 4 and k = 4, then counting down from the r

th largest 

observation if there are two ties within r = 4 and two ties not in r, increase r by 2 and subsequently 

increase k by 2. The modified r and k values to be used will be now 6. 

STEP 5: If the m and n are not multiples of 5, then the closest values of m and n are entered in the table to 

get the values of r, k and α. The formula in Step 1 is then used to find the value of the actual α with the 

original m and n values. If the actual α value is close enough to the desired α value, the quantile test is 

conducted with r and k from the rounded m and n values. For an example, for a required α level of 0.01 

and with m = 47 and n = 77, the closest tabled values are m = 45 and n = 75. For m = 45 and n = 75, 

tabled values are r = 9, k = 9, and α = 0.012. To check if the quantile test can be performed, compute the 

actual α value as shown below: 

⎛124 −9⎞⎛9⎞

⎝ − ⎜ ⎜ ⎟ 77 9 ⎟ 9

Actual Alpha = α = ⎛124 

⎠

⎞

⎝ ⎠ = 0.012 

⎜ ⎟ ⎝ 77 ⎠

Since 0.012 is close enough to 0.01, it is safe to conduct the quantile test. 

STEP 6: For r < 20, compute the probability P as shown in Step 1. For r ≥ 20, use the normal 

approximation and calculate Z as shown below: 

k − 0.5 − X 

z = (6-13) sd 

1 

nr ⎡ mnr (m + +n r ) ⎤ 2 

where X = m n +

 and sd = ⎢

⎢ mn 

2 )

⎥

⎣( + ) (m n + −1 ⎥⎦

168 
STEP 7: Conclusion: 

When r < 20, 

Form 1: If computed k ≥ # of Site Observations in r, then reject the null hypothesis that the site population 

parameter is less than or equal to the background population parameter. 

When r ≥ 20, 

Form 1: If P – value < specified α, then reject the null hypothesis that the site population parameter is less 

than or equal to the background population parameter. The details are given in EPA (1994). 

169 
170 
Chapter 7 

Outlier Tests for Data Sets with and without Nondetect Values 

Outliers are measurements that are extremely large or small relative to the rest of the data and, therefore, 

are suspected of misrepresenting the main dominant population from which they were collected. Outliers 

are inevitable in most environmental applications. Outliers may result from gross errors, such as 

transcription errors, data-coding errors, or measurement system problems such as instrument breakdown. 

However, outliers may also represent true extreme values of a distribution (for instance, hot spots) and 

indicate more variability in the population than was expected. Typically, outliers represent observations 

coming from population(s) different from the main dominant population (e.g., a background area, a site 

area after cleanup) under study. Typically, outliers represent low probability observations coming from 

the tails of the data distribution under consideration (e.g., data from an AOC). The presence of outliers in 

a data set distorts the computations of all classical statistics (e.g., sample mean, standard deviation, upper 

prediction, and upper tolerance limits, test statistics, GOF statistics, and also outlier test statistics) of 

interest. Some description (with examples) of the influence of outliers on the computation of various 

statistics, including the sample mean, sample standard deviation and several upper limits, is given in 

Chapters 3 and 5 of the revised Guidance for Comparing Background and Chemical Concentrations in 

Soil for CERCLA Sites (EPA, 2002b). The use of such distorted statistics (e.g., two-sample t-test, GOF 

test) may lead to incorrect cleanup decisions about the site under consideration. 

Statistical tests based on parametric methods generally are more sensitive to the existence of outliers in 

either the site or the background data sets than are those based on nonparametric distribution-free 

methods. The use of nonparametric hypotheses tests for background comparisons can reduce (but not 

completely) the sensitivity of test results to the presence of outliers to a certain extent. Specifically, 

nonparametric statistics (e.g., UTLs, UPLs) based upon higher order statistics (e.g., largest, second 

largest) will represent outlying observations. Outliers can also lead to both Type I and Type II errors by 

distorting the test statistics used for hypotheses testing about the population parameters (e.g., means). 

They can lead to inconclusive and potentially incorrect results if the test statistics are sensitive to the 

outliers. This issue is illustrated below by using an example, comparing site versus background lead 

concentrations collected from a Superfund site. 

In environmental applications, it is important to identify high outlying observations, as those high outliers 

may represent contaminated (or hot areas) locations of a polluted site. Typically, it is the presence of a 

few high outlying observations that distort the normality of a data set. That is, in practice, many data sets 

follow a normal distribution after the removal of potential outliers. Unfortunately such data sets with a 

few high outliers (often representing contaminated site areas) can also be modeled by a lognormal 

distribution. It is observed that in practice, the use of a log-transformation tends to hide and accommodate 

outlier(s) as part of the majority of the data set representing the dominant population. Statistical methods 

used on such contaminated data sets will yield distorted statistics and estimates (e.g., site average) for the 

entire site area under investigation. For an example, inclusion of a few high outliers (perhaps representing 

a polluted part of the site) in the computation of a UCL95 will yield a distorted estimate of the EPC for 

the entire site area under investigation. Decisions based upon such distorted statistics can be expensive 

and (or) not protective of the human health and the environment. In environmental applications the 

objective is to expose and identify such outlying observations as those observations often represent hot 

spots and (or) contaminated areas of the site, or observations possibly collected from an area other than 

the site background. These observations need to be identified as those requiring separate and further 

investigation and remediation. The project team and the decision makers involved in the project decision 

171 
should decide about the proper disposition (include or not include) of high outlying observations. 

Sometimes, performing statistical analyses twice on the same data set – once on using the full data set 

with outliers and once on data set without high outliers can help a typical user in determining the 

disposition of high outliers. 

Statistical outlier tests give the analyst probabilistic evidence that an extreme value (potential outlier) 

does not “fit” with the distribution of the remainder of the data and is therefore a statistical outlier. These 

tests should only be used to identify data points that require further investigation. The classical outlier 

tests should be accompanied by graphical displays such as, Q-Q plots and box plots. Graphical displays 

provide additional insight into a data set that cannot be revealed by tests statistics (e.g., Rosner test, Dixon 

test, Shapiro Wilk test) alone. The statistical tests (e.g., Rosner test) alone cannot determine whether a 

statistical outlier should be discarded or corrected within a data set; this decision should be based on 

judgmental or scientific grounds. Typically, there are 5 steps involved in treating extreme values or 

outliers: 

1. Identify extreme high values that may be potential outliers, 

2. Apply a statistical test and supplement them with graphical displays, 

3. Scientifically review the statistical outliers and decide on their proper disposition, 

4. Conduct data analyses with and without the statistical outliers, and 

5. Document the entire process. 

The final disposition of outliers should be a team effort including the project team, decision makers, and 

experts familiar with the site conditions. As mentioned before, potential outliers may be identified 

through the graphical representations. Graphs, such as the box and goodness-of-fit (GOF) Q-Q plot, can 

be used to identify observations that are much larger or smaller than the rest of the data. If potential 

outliers are identified, then the next step is to apply one of the statistical tests described in the following 

sections. 

If a data point is found to be an outlier, then the analyst may either: 1) correct the data point; 

2) isolate the data point for further investigation; or 3) use the data point in all of the analyses. This 

decision should be based on scientific reasoning in addition to the results of the statistical test. For 

instance, data points containing transcription errors should be corrected, whereas data points collected 

while an instrument was malfunctioning may be discarded. As mentioned before, the disposition of 

outlier(s) should be a team effort. The project team should assess the influence (often undue influence) of 

outliers on the estimates (e.g., EPC, BTV) and test statistics (e.g., t-test) to be computed. It is noted that 

even the presence of a single outlier can distort all statistics of interest such as averages, standard 

deviations (sd), and various upper limits and test statistics. One simple way to assess the influence of 

outliers on the statistics to be computed is to compute the statistics with and without the high outlying 

observations. In other words, if a high outlier is discarded from the data set, then it is desirable that all of 

the statistical analysis of the data should be applied to both the full (with outliers) and the truncated data 

set (without high outliers) so that the influence of outliers may be assessed. For an example, if the 

difference between a UCL95 based upon a full data set with outliers is significantly higher (as often is the 

case) from the UCL95 based upon the data set without the high outlying observations, then the project 

team should be able to decide which of the two UCL values represents a more realistic estimate of the 

EPC term. 

172 
The practitioner should also be clear about the objective of the study. Typically, in such applications, the 

objective is to compute the statistics (e.g., averages, UCLs, UPLs) based upon the majority of the data set 

representing the dominant population. For an example, the average (estimate of population mean) should 

be a representative of the population based upon the majority of the data representing the dominant (main) 

population. A distorted estimate accommodating a few outliers tends to represent that contaminated area 

of the site (and not the entire dominant site area under consideration). Therefore, it is desirable that the 

project team decides to isolate a few high outliers and investigate their locations for the possibility of 

further remediation. A couple of classical outliers tests (Rosner test and Dixon test) often cited in 

environmental literature have been incorporated in ProUCL 4.0. These tests can be used on data sets with 

and without nondetect observations. 

7.1 Outlier Tests for Data Sets without Nondetect Observations 

A couple of classical outlier test procedures often used in environmental applications (EPA, 2006, and 

Gilbert, 1987) are briefly described here. It is noted that these classical tests do suffer from masking 

effects and may fail to identify potential outliers present in a data set. This is especially true when 

multiple outliers or multiple populations may be present in a data set. Such scenarios can be revealed by 

graphical displays, such as a Q-Q plot discussed earlier. More effective robust outlier identification 

procedures (Singh and Nocerino, 1995) are beyond the scope of ProUCL 4.0. Several robust estimation 

and outlier identification procedures are available in Scout software package (EPA, 1999), which is 

currently being upgraded. 

7.1.1 Dixon’s Test 

Dixon’s Extreme Value test (1953) can be used to test for statistical outliers when the sample size is less 

than or equal to 25. It is noted that Dixon’s test considers both extreme values that are much smaller than 

the rest of the data (Case 1) and extreme values that are much larger than the rest of the data (Case 2). 

This test assumes that the data without the suspected outlier are normally distributed; therefore, it is 

necessary to perform a test for normality on the data without the suspected outlier before applying this 

test. This means that the user has to identify (guess) potential outliers that may be present in the data set. 

One simple way to identify and look at outliers is the use of graphical displays such as a Q-Q plot and box 

plot. The Dixon test often suffers from masking effects when more than one outlier may be present in the 

data set. If more than one outlier is suspected, the Dixon test may lead to masking where two or more 

outliers close in value “hide” one another. As mentioned before, the use of robust and resistant outlier 

procedures (Singh and Nocerino, 1995, Rousseeuw and Leroy, 1987, and Scout, 1999) is desirable. 

However, robust and resistant methods are beyond the scope of ProUCL 4.0. Several robust methods are 

available in Scout (EPA, 1999) software package, which is currently under revision and upgrade. 

Even though Dixon’s test finds outliers in both tails (low and high outliers) of the data distribution, it is 

the identification of high outlying observations (perhaps representing contamination), which is important 

in environmental applications. The inclusion of high outliers in a data set results in distorted statistics of 

interest, including estimates and test statistics. The low identified outliers (if any) may be retained in a 

data set to compute various statistics of interest. 

7.1.1.1 Directions for the Dixon’s Test 

STEP 1: Let X(1), X(2), . . . , X(n) represent the data ordered from smallest to largest. Check that the data 

without the suspect outlier are normally distributed. If normality fails, then apply a different outlier 

identification method such as a robust outlier identification procedure. It is suggested to avoid the use of a 

173 
transformation such as a log-transformation to achieve normality to be able to use the Dixon test. All 

cleanup and remediation decisions are made based upon the data set in raw scale. Therefore, outliers 

perhaps representing isolated contaminated locations should be identified in the original scale. As 

mentioned before, the use of a log-transformation tends to hide and accommodate outliers (instead of 

identifying them). 

STEP 2: X(1) is a potential outlier (Case 1): Compute the test statistic, C, where 

X (2) − X (1) X (3) − X (1) C = for 3 ≤ n ≤ 7, C = for 11 ≤ n ≤ 13,

X (n) − X (1) X (n−1) − X (1) 

X (2) − X (1) X (3) − X (1) C = for 8 ≤ n ≤ 10, C = for 14 ≤ n ≤ 25,

X (n−1) − X (1) X (n−2) − X (1) 

STEP 3: If C exceeds the critical value for the specified significance level α, then X(1) is an outlier and 

should be further investigated. 

STEP 4: X(n) is a potential outlier (Case 2): Compute the test statistic, C, where 

X (n) − X (n−1) X (n) − X (n−2) C = for 3 ≤ n ≤ 7, C = for 11 ≤ n ≤ 13, X (n) − X (1) X (n) − X (2) 

X (n) − X (n−1) X (n) − X (n−2) C = for 8 ≤ n ≤ 10, C = for 14 ≤ n ≤ 25,

X (n) − X (2) X (n) − X (3) 

STEP 5: If C exceeds the critical value for the specified significance level α, then X(n) is an outlier and 

should be further investigated. 

7.1.2 Rosner’s Test 

A parametric test developed by Rosner can be used to detect up to 10 outliers for sample sizes of 25 or 

more. The details of the test can be found in Gilbert (1987). This test also assumes that the data are 

normally distributed without the outliers; therefore, it is necessary to perform a test for normality before 

applying this test. Note that the test assumes that, the data without the outliers are normally distributed; 

therefore, the test for normality has to be performed without the suspected outliers, which need to be 

identified or determined first. A graphical display (Q-Q plot) can help determine to identify suspected 

outliers. 

7.1.2.1 Directions for the Rosner’s Test 

To apply Rosner’s test, first determine an upper limit, r0, on the number of outliers (r0 ≤ 10), then order 

the r0 extreme values from most extreme to least extreme. Rosner’s test statistic is computed using the 

sample mean and sample standard deviation. 

STEP 1: Let X1, X2, . . . , Xn represent the ordered data points. By inspection, identify the maximum 

number of possible outliers, r0. Check that the data are normally distributed. 

174 
STEP 2: Compute the sample mean, x , and the sample standard deviation, s, for all the data. Label these 

values x (0) and s(0) , respectively. Determine the value that is farthest from x (0) and label this 

observation y(0) . Delete y(0) from the data and compute the sample mean, labeled x (1) , and 

the sample standard deviation, labeled s(1) . Then determine the observation farthest from x (1) 

and label this observation y(1) . Delete y(1) and compute x (2) and s(2) . Continue this process 

until r0 extreme values have been eliminated. 

After carrying out the above process, the analyst should have 

[ x (0) 

, s(0) , y(0)]; [ x (1) , s(1) , y(1)]; ..., [ x (r0 −1) , s(r0 −1) , y(r0 −1) ] where 

n−i n−i 

x (i) = 1 ∑ x j , s(i) = 1 ∑(x j − x (i)

)

2 , and y(i) is the farthest value x (i)

. n − i j=1 n − i j=1 

Note: The above formulae for x (i) and s(i) assume that the data have been renumbered after 

each outlying observation is deleted. 

STEP 3: To test if there are “r” outliers in the data, compute: Rr = | y(r −1

s 

)

( 

−

r−1

x 

)

(r−1) | 

 and compare Rr to 

the critical value λr in the tables from any statistical literature. If Rr ≥ λr , conclude that there 

are r outliers. 

First, test if there are r0 outliers (compare Rr0 −1 to λr0 −1 ). If not, then test if there are r0 - 1 

outliers (compare Rr0 −2 to λr0 −2 ). If not, then test if there are r0 - 2 outliers, and continue, until 

either it is determined that there are a certain number of outliers or that there are no outliers at 

all. 

7.2 Outlier Tests for Data Sets with Nondetect Observations 

For the purpose of the identification of high outliers, one may replace the nondetect values by their 

respective detection limits or may just ignore them (especially when the number of detected values is 

large such as exceeding 8-10) from any of the outlier test (e.g., Rosner test) computation, including the 

graphical displays such as Q-Q plots. Both of these procedures (ignoring NDs, or replacing them by 

DL/2) for outliers testing are available in ProUCL for data sets with ND values. Note that outlier 

identification procedures represent exploratory tools and are used for pre-processing of a data set to 

identify outliers or multiple populations that may be present in a data set. Except for the identification of 

high outlying observations, the outlier identification statistics (computed with NDs or without NDs) are 

not used in any of the estimation and decision making process. Therefore, for the purpose of the 

identification of high outliers, it does not matter how the nondetect observations are treated. After outliers 

have been identified, the project team and experts familiar with the site should make the final decision 

about the disposition of outliers. 

Thus, nondetects and outliers are inevitable in most environmental data sets. Typically, the objective is to 

identify high outlying contaminating observations, the same two classical tests described above may be 

175 
used to identify outliers in data sets with nondetects observations. ProUCL 4.0 offers two options to test 

for outliers that may be present in the upper tail of the data distribution. The user can use Dixon test or 

Rosner test to test for outliers using the data set excluding all nondetect observations. Alternatively, the 

user may replace all nondetects by their respective DL/2 values, and use Dixon test or Rosner test on the 

resulting data set to test for outliers. This option should be used as an exploratory tool to identify outliers 

(if any). In any case, it is always desirable to use graphical displays to visually look at the outliers. 

ProUCL 4.0 can be used to compute Q-Q plot and box plot for data sets with nondetect observations. An 

example illustrating the improper influence of outliers on test statistic such as t-test statistic is considered 

as follows. 

Example. At a Superfund site many inorganic compounds were analyzed to perform site versus 

background comparisons. In this example, background and site lead concentrations are being compared. 

This example is included here to illustrate how outliers influence the tests statistics and in turn leading to 

potentially incorrect inclusions. The background data set has only 6 data points (which are not enough to 

perform two-sample comparisons) and the site data set has 10 data points. This complete data set is 

included in Appendix 3 of the revised Guidance for Comparing Background and Chemical 

Concentrations in Soil for CERCLA Sites (EPA, 2002b). The site data set seems to have a couple of 

outliers. This example illustrates how test statistics get distorted by outliers leading to potentially 

incorrect conclusions. Therefore, as mentioned before, it is desirable to investigate the outliers separately. 

The two-sample t-test results and nonparametric WMW test results for the background versus site 

comparisons are presented in the following tables. The parametric and nonparametric test results for full 

data set (with outliers) are respectively, summarized in Table 7-1 (t-test) and Table 7-2 (WMW test). 

Table 7-3 has outlier test results for site data set based upon Dixon test. The parametric and 

nonparametric tests were performed again using the site data set without the outliers. Those test results 

are summarized in Table 7-4 (t-test) and Table 7-5 (WMW test). All statistics have been obtained using 

ProUCL 4.0 software package. 

176 
Table 7-1. Parametric Site vs. Background Comparison for Lead Data with Outliers 

From Table 7-1, it is noted that, Student’s t-test statistic got distorted by outliers leading to the incorrect 

conclusion that the mean lead concentrations of site and background populations are comparable. 

However, the nonparametric WMW test statistic (Table 7-2) lead to the correct conclusion that the site 

lead concentration levels are significantly higher than the background lead concentration levels. In order 

to avoid the use of distorted test statistics and deriving incorrect conclusions, it is always desirable to 

supplement the formal tests results with graphical displays. It is noted that a formal test statistic (e.g., ttest) alone cannot determine if the outliers are present and the conclusions derived are correct or incorrect. 

A quick look at the side-by-side box plot (Figure 7-1) of site and background lead data reveals that the 

site concentrations are significantly higher than the background concentrations. Next, Table 7-3 

summarizes the results of Dixon’s outlier test on site lead data set. One outlier (1940) was identified. 

177 
Figure 7-1. Side-by-Side Box Plots for Lead from Background and Site Areas 

Table 7-2. Nonparametric Site vs. Background Comparison for Lead with Outliers 

178 
Table 7-3. Dixon Outlier Test Results for Site Lead Data Set 

The parametric test (Table 7-4) and nonparametric test (Table 7-5) were performed again to compare the 

site and background lead concentrations using site data set without the outlier, 1940. The parametric t-test 

(Table 7-4) still leads to the incorrect conclusion that site and background concentrations are comparable. 

However, the nonparametric WMW (Table 7-5) test again leads to the correct conclusion that the site lead 

concentrations are higher than those of the background lead concentrations. This example illustrates that 

when the underlying assumptions are not met (e.g., normality) or outliers are present, the parametric test 

can result in incorrect conclusions. 

179 
Table 7-4. Parametric Site vs. Background Comparison for Lead Data without Outlier 

180 
Table 7-5. Nonparametric Site vs. Background Comparison for Lead without Outlier 

181 
182 
Appendix 

Simulated Critical Values for Gamma GOF Tests, the AndersonDarling Test and the Kolmogorov-Smirnov Test 

Simulation Experiments 

The simulation experiments performed are briefly described here. The experiments were carried out for 

various values of the sample size, n = 5(25)1, 30(50)5, 60(100)10, 200(500)100, and 1000. Random 

deviates of sample size n were generated from a gamma, (k, θ), population. Various values of k have been 

considered. The considered values of the shape parameter, k, are: 0.01, 0.025, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 

5.0, 10.0, 20.0, and 50.0. These values of k cover a wide range of values of skewness, 2/√k. The 

distributions of the Kolmogorov-Smirnov (K-S) test statistic, D, and the Anderson-Darling (A-D) test 

statistic, A2

, do not depend upon the scale parameter, θ , therefore, the scale parameter, θ , has been set 

equal to 1 in all of the simulation experiments. A typical simulation experiment can be described in the 

following four steps. Some details of the gamma deviate generation methods and the maximum likelihood 

estimation methods can be found in Singh, Singh, and Iaci (EPA, 2002). 

Step 1. Generate a random sample of the specified size, n, from a gamma, G (k, 1), distribution. The 

algorithm as outlined in Whittaker (1974) has been used to generate the gamma deviates. 

Step 2. For each generated sample, compute the MLEs of k and θ (Choi and Wette, 1969), and the K-S 

and the A-D test statistics (Anderson and Darling, 1954, D’Agostino and Stephens, 1986, and 

Schneider and Clickner, 1976) using the incomplete gamma function. 

Step 3. Repeat Steps 1 and 2, 20,000 times. 

Step 4. Arrange the resulting test statistics in ascending order. Compute the 80%, 90%, 95%, and 99% 

percentiles of the K-S test statistic and the A-D test statistic. 

The resulting raw 20%, 10%, 5%, and 1% critical values for the two EDF tests are summarized in Tables 

1 through 8 of this Appendix. The critical values as summarized in Tables 1-8 are in agreement (up to 3 

significant digits) with all available exact or asymptotic critical values. It is also noted that the critical 

values for the K-S test statistic are more stable than those for the A-D test statistic. This is especially true 

when the shape parameter, k, is small and the sample size, n, is large. It is hoped that the availability of 

the critical values for the GOF tests for the gamma distribution will result in the frequent use of more 

practical and appropriate gamma distributions in environmental and other applications. 

183 
Table 1. Critical Values for A-D Test Statistic for Significance Level = 0.20 

n\k 0.010 0.025 0.050 0.10 0.20 0.50 1.0 2.0 5.0 10.0 20.0 50.0 

5 0.637 0.609 0.580 0.559 0.532 0.505 0.497 0.493 0.492 0.491 0.490 0.490 

6 0.685 0.636 0.591 0.568 0.543 0.512 0.503 0.498 0.496 0.496 0.495 0.495 

7 0.735 0.667 0.604 0.575 0.549 0.517 0.506 0.501 0.499 0.498 0.498 0.497 

8 0.786 0.697 0.615 0.578 0.555 0.521 0.509 0.504 0.502 0.500 0.500 0.499 

9 0.839 0.729 0.626 0.583 0.559 0.524 0.512 0.507 0.502 0.502 0.502 0.501 

10 0.892 0.760 0.638 0.585 0.563 0.526 0.513 0.508 0.504 0.503 0.502 0.502 

15 1.166 0.922 0.693 0.595 0.574 0.533 0.519 0.511 0.507 0.506 0.505 0.505 

16 1.220 0.954 0.705 0.597 0.574 0.534 0.520 0.513 0.508 0.507 0.506 0.505 

17 1.275 0.986 0.716 0.597 0.576 0.535 0.520 0.512 0.508 0.507 0.506 0.505 

18 1.327 1.019 0.726 0.599 0.576 0.536 0.520 0.513 0.509 0.507 0.507 0.506 

19 1.380 1.050 0.738 0.600 0.578 0.537 0.521 0.513 0.508 0.507 0.507 0.507 

20 1.432 1.081 0.747 0.602 0.578 0.537 0.521 0.513 0.509 0.508 0.507 0.507 

21 1.486 1.112 0.757 0.602 0.579 0.539 0.521 0.514 0.509 0.508 0.508 0.507 

22 1.537 1.143 0.768 0.604 0.579 0.538 0.522 0.513 0.509 0.508 0.507 0.507 

23 1.588 1.177 0.779 0.604 0.580 0.538 0.521 0.513 0.510 0.508 0.507 0.507 

24 1.641 1.206 0.789 0.605 0.581 0.539 0.522 0.514 0.509 0.508 0.508 0.507 

25 1.692 1.238 0.800 0.606 0.581 0.539 0.523 0.514 0.510 0.508 0.507 0.507 

30 1.943 1.390 0.853 0.611 0.583 0.540 0.523 0.515 0.510 0.509 0.508 0.507 

35 2.190 1.537 0.906 0.615 0.584 0.541 0.524 0.516 0.510 0.509 0.509 0.508 

40 2.432 1.683 0.955 0.617 0.585 0.542 0.525 0.515 0.511 0.509 0.508 0.508 

45 2.673 1.828 1.005 0.621 0.586 0.542 0.524 0.516 0.511 0.509 0.508 0.508 

50 2.906 1.967 1.054 0.624 0.587 0.541 0.525 0.516 0.511 0.510 0.509 0.509 

60 3.368 2.246 1.150 0.631 0.588 0.543 0.525 0.516 0.511 0.510 0.508 0.509 

70 3.826 2.518 1.248 0.636 0.588 0.543 0.525 0.516 0.511 0.510 0.509 0.509 

80 4.273 2.785 1.343 0.642 0.589 0.544 0.526 0.517 0.511 0.510 0.509 0.509 

90 4.719 3.053 1.437 0.647 0.588 0.544 0.526 0.517 0.511 0.510 0.509 0.509 

100 5.166 3.314 1.532 0.652 0.589 0.544 0.526 0.517 0.512 0.510 0.509 0.509 

200 9.462 5.855 2.420 0.706 0.591 0.545 0.526 0.517 0.511 0.509 0.509 0.509 

300 13.65 8.320 3.273 0.760 0.591 0.545 0.526 0.517 0.512 0.511 0.509 0.509 

400 17.78 10.73 4.107 0.812 0.590 0.545 0.527 0.517 0.513 0.511 0.510 0.509 

500 21.87 13.12 4.923 0.865 0.591 0.545 0.527 0.517 0.512 0.510 0.509 0.509 

1000 42.04 24.87 8.900 1.123 0.592 0.546 0.526 0.517 0.512 0.510 0.510 0.509 

184 
Table 2. Critical Values for K-S Test Statistic for Significance Level = 0.20 

n\k 0.010 0.025 0.050 0.10 0.20 0.50 1.0 2.0 5.0 10.0 20.0 50.0 

5 0.349 0.341 0.332 0.328 0.323 0.313 0.307 0.304 0.303 0.302 0.301 0.301 

6 0.335 0.322 0.310 0.305 0.299 0.289 0.284 0.281 0.279 0.279 0.279 0.279 

7 0.321 0.306 0.292 0.285 0.279 0.270 0.265 0.262 0.261 0.260 0.260 0.260 

8 0.310 0.293 0.276 0.268 0.264 0.255 0.250 0.247 0.246 0.245 0.245 0.244 

9 0.301 0.283 0.264 0.255 0.251 0.242 0.237 0.235 0.233 0.232 0.232 0.232 

10 0.294 0.274 0.253 0.244 0.239 0.231 0.226 0.224 0.222 0.221 0.221 0.221 

15 0.266 0.243 0.216 0.203 0.199 0.192 0.188 0.185 0.184 0.183 0.183 0.183 

16 0.262 0.238 0.211 0.197 0.193 0.186 0.182 0.180 0.179 0.178 0.178 0.178 

17 0.259 0.234 0.206 0.191 0.188 0.181 0.177 0.175 0.174 0.173 0.173 0.173 

18 0.255 0.230 0.201 0.186 0.183 0.176 0.172 0.170 0.169 0.168 0.168 0.168 

19 0.252 0.227 0.197 0.182 0.179 0.172 0.168 0.166 0.165 0.164 0.164 0.164 

20 0.249 0.224 0.194 0.177 0.174 0.168 0.164 0.162 0.161 0.160 0.160 0.160 

21 0.246 0.220 0.190 0.173 0.170 0.164 0.160 0.158 0.157 0.157 0.157 0.156 

22 0.244 0.218 0.187 0.170 0.167 0.160 0.157 0.155 0.154 0.153 0.153 0.153 

23 0.241 0.215 0.184 0.166 0.163 0.157 0.154 0.152 0.150 0.150 0.150 0.150 

24 0.239 0.212 0.181 0.163 0.160 0.154 0.151 0.149 0.147 0.147 0.147 0.147 

25 0.237 0.210 0.178 0.160 0.157 0.151 0.148 0.146 0.145 0.144 0.144 0.144 

30 0.227 0.200 0.167 0.147 0.144 0.138 0.135 0.134 0.133 0.132 0.132 0.132 

35 0.220 0.192 0.158 0.137 0.134 0.129 0.126 0.124 0.123 0.123 0.123 0.123 

40 0.214 0.186 0.151 0.128 0.125 0.121 0.118 0.116 0.116 0.115 0.115 0.115 

45 0.208 0.180 0.145 0.121 0.119 0.114 0.112 0.110 0.109 0.109 0.109 0.109 

50 0.204 0.176 0.140 0.115 0.113 0.108 0.106 0.105 0.104 0.103 0.103 0.103 

60 0.197 0.168 0.132 0.106 0.103 0.099 0.097 0.096 0.095 0.095 0.095 0.095 

70 0.191 0.162 0.126 0.099 0.096 0.092 0.090 0.089 0.088 0.088 0.088 0.088 

80 0.187 0.158 0.121 0.093 0.090 0.086 0.084 0.083 0.083 0.082 0.082 0.082 

90 0.183 0.154 0.117 0.088 0.085 0.081 0.080 0.079 0.078 0.078 0.078 0.078 

100 0.180 0.150 0.113 0.084 0.080 0.077 0.076 0.075 0.074 0.074 0.074 0.074 

200 0.163 0.132 0.094 0.061 0.057 0.055 0.054 0.053 0.053 0.053 0.053 0.052 

300 0.155 0.125 0.086 0.051 0.047 0.045 0.044 0.044 0.043 0.043 0.043 0.043 

400 0.151 0.120 0.081 0.046 0.041 0.039 0.038 0.038 0.037 0.037 0.037 0.037 

500 0.148 0.117 0.077 0.042 0.036 0.035 0.034 0.034 0.034 0.033 0.033 0.033 

1000 0.141 0.109 0.069 0.032 0.026 0.025 0.024 0.024 0.024 0.024 0.024 0.024 

185 
Table 3. Critical Values for A-D Test Statistic for Significance Level = 0.10 

n\k 0.010 0.025 0.050 0.10 0.20 0.50 1.0 2.0 5.0 10.0 20.0 50.0 

5 0.761 0.731 0.705 0.691 0.654 0.612 0.599 0.594 0.591 0.589 0.589 0.588 

6 0.824 0.772 0.726 0.707 0.672 0.625 0.610 0.603 0.599 0.599 0.598 0.598 

7 0.891 0.813 0.742 0.716 0.684 0.635 0.618 0.609 0.607 0.606 0.604 0.605 

8 0.957 0.854 0.760 0.725 0.694 0.641 0.624 0.616 0.612 0.610 0.609 0.608 

9 1.026 0.896 0.777 0.731 0.701 0.648 0.629 0.620 0.614 0.613 0.613 0.612 

10 1.094 0.938 0.792 0.736 0.707 0.652 0.632 0.623 0.618 0.616 0.615 0.614 

15 1.431 1.146 0.868 0.752 0.724 0.663 0.642 0.630 0.624 0.622 0.621 0.621 

16 1.496 1.188 0.885 0.755 0.725 0.665 0.642 0.632 0.626 0.624 0.622 0.621 

17 1.559 1.226 0.899 0.757 0.727 0.666 0.644 0.632 0.626 0.623 0.623 0.622 

18 1.622 1.267 0.913 0.760 0.729 0.668 0.643 0.634 0.626 0.623 0.624 0.623 

19 1.683 1.304 0.928 0.761 0.730 0.670 0.645 0.633 0.626 0.625 0.624 0.624 

20 1.744 1.344 0.941 0.763 0.732 0.669 0.645 0.633 0.627 0.626 0.624 0.624 

21 1.808 1.380 0.954 0.765 0.732 0.671 0.646 0.634 0.628 0.626 0.626 0.624 

22 1.865 1.417 0.968 0.767 0.734 0.670 0.646 0.636 0.628 0.627 0.625 0.625 

23 1.925 1.459 0.983 0.768 0.735 0.671 0.645 0.635 0.629 0.627 0.625 0.625 

24 1.985 1.494 0.995 0.770 0.736 0.672 0.647 0.635 0.628 0.627 0.626 0.625 

25 2.043 1.528 1.009 0.770 0.736 0.673 0.648 0.636 0.629 0.627 0.626 0.625 

30 2.330 1.709 1.076 0.778 0.738 0.674 0.650 0.637 0.629 0.628 0.627 0.626 

35 2.606 1.881 1.141 0.783 0.739 0.676 0.650 0.638 0.631 0.629 0.628 0.627 

40 2.879 2.046 1.202 0.787 0.742 0.677 0.651 0.637 0.631 0.629 0.628 0.628 

45 3.140 2.209 1.260 0.793 0.743 0.677 0.651 0.639 0.632 0.630 0.628 0.629 

50 3.398 2.367 1.320 0.797 0.746 0.677 0.652 0.640 0.632 0.630 0.629 0.629 

60 3.903 2.677 1.435 0.806 0.747 0.679 0.652 0.640 0.632 0.631 0.629 0.629 

70 4.394 2.979 1.550 0.811 0.747 0.679 0.653 0.641 0.633 0.630 0.630 0.630 

80 4.882 3.269 1.660 0.820 0.747 0.680 0.654 0.641 0.633 0.631 0.630 0.629 

90 5.358 3.563 1.766 0.827 0.748 0.680 0.654 0.642 0.634 0.631 0.629 0.630 

100 5.838 3.848 1.874 0.833 0.749 0.681 0.654 0.642 0.633 0.631 0.630 0.630 

200 10.37 6.570 2.861 0.902 0.751 0.682 0.654 0.642 0.634 0.631 0.631 0.630 

300 14.74 9.168 3.786 0.969 0.752 0.682 0.655 0.641 0.634 0.633 0.631 0.630 

400 19.03 11.69 4.679 1.032 0.751 0.683 0.655 0.641 0.635 0.633 0.631 0.631 

500 23.26 14.19 5.551 1.095 0.752 0.683 0.655 0.643 0.635 0.632 0.631 0.631 

1000 43.96 26.32 9.737 1.399 0.752 0.684 0.655 0.643 0.635 0.632 0.631 0.630 

186 
Table 4. Critical Values for K-S Test Statistic for Significance Level = 0.10 

n\k 0.010 0.025 0.050 0.10 0.20 0.50 1.0 2.0 5.0 10.0 20.0 50.0 

5 0.386 0.376 0.368 0.366 0.359 0.346 0.339 0.336 0.334 0.333 0.333 0.333 

6 0.369 0.357 0.345 0.339 0.332 0.319 0.313 0.310 0.307 0.307 0.307 0.307 

7 0.356 0.339 0.323 0.318 0.313 0.301 0.294 0.290 0.288 0.288 0.287 0.287 

8 0.344 0.327 0.308 0.301 0.296 0.284 0.278 0.274 0.272 0.271 0.271 0.271 

9 0.334 0.315 0.295 0.286 0.281 0.270 0.264 0.260 0.258 0.257 0.257 0.257 

10 0.326 0.305 0.282 0.273 0.268 0.257 0.251 0.248 0.246 0.245 0.245 0.245 

15 0.294 0.270 0.241 0.227 0.223 0.214 0.209 0.206 0.204 0.204 0.203 0.203 

16 0.290 0.265 0.236 0.221 0.217 0.208 0.203 0.200 0.198 0.198 0.197 0.197 

17 0.285 0.260 0.230 0.214 0.211 0.202 0.197 0.194 0.193 0.192 0.192 0.192 

18 0.281 0.256 0.225 0.209 0.205 0.197 0.192 0.189 0.188 0.187 0.187 0.187 

19 0.278 0.252 0.221 0.204 0.200 0.192 0.187 0.184 0.183 0.182 0.182 0.182 

20 0.274 0.248 0.217 0.199 0.196 0.187 0.183 0.180 0.179 0.178 0.178 0.178 

21 0.271 0.245 0.212 0.195 0.191 0.183 0.179 0.176 0.175 0.174 0.174 0.174 

22 0.268 0.241 0.209 0.190 0.187 0.179 0.175 0.172 0.171 0.170 0.170 0.170 

23 0.265 0.238 0.206 0.186 0.183 0.175 0.171 0.169 0.167 0.167 0.166 0.166 

24 0.262 0.235 0.202 0.183 0.180 0.172 0.168 0.165 0.164 0.163 0.163 0.163 

25 0.259 0.232 0.199 0.179 0.176 0.169 0.165 0.162 0.161 0.160 0.160 0.160 

30 0.248 0.221 0.187 0.165 0.162 0.155 0.151 0.149 0.147 0.147 0.147 0.147 

35 0.239 0.212 0.177 0.153 0.150 0.144 0.140 0.138 0.137 0.136 0.136 0.136 

40 0.232 0.204 0.168 0.144 0.141 0.135 0.132 0.130 0.128 0.128 0.128 0.128 

45 0.226 0.198 0.161 0.136 0.133 0.127 0.124 0.122 0.121 0.121 0.121 0.121 

50 0.221 0.192 0.156 0.130 0.127 0.121 0.118 0.116 0.115 0.115 0.115 0.115 

60 0.213 0.184 0.147 0.119 0.116 0.111 0.108 0.107 0.106 0.105 0.105 0.105 

70 0.206 0.177 0.139 0.111 0.107 0.103 0.100 0.099 0.098 0.098 0.097 0.097 

80 0.201 0.171 0.134 0.104 0.101 0.096 0.094 0.093 0.092 0.092 0.091 0.091 

90 0.196 0.166 0.129 0.099 0.095 0.091 0.089 0.088 0.087 0.086 0.086 0.086 

100 0.193 0.162 0.125 0.094 0.090 0.086 0.084 0.083 0.082 0.082 0.082 0.082 

200 0.172 0.141 0.102 0.069 0.064 0.062 0.060 0.059 0.059 0.058 0.058 0.058 

300 0.163 0.132 0.093 0.058 0.053 0.050 0.049 0.048 0.048 0.048 0.048 0.048 

400 0.157 0.126 0.087 0.051 0.046 0.044 0.043 0.042 0.042 0.042 0.041 0.041 

500 0.154 0.123 0.083 0.047 0.041 0.039 0.038 0.038 0.037 0.037 0.037 0.037 

1000 0.145 0.113 0.073 0.036 0.029 0.028 0.027 0.027 0.026 0.026 0.026 0.026 

187 
Table 5. Critical Values for A-D Test Statistic for Significance Level = 0.05 

n\k 0.010 0.025 0.050 0.10 0.20 0.50 1.0 2.0 5.0 10.0 20.0 50.0 

5 0.873 0.846 0.830 0.826 0.775 0.711 0.691 0.684 0.681 0.679 0.679 0.678 

6 0.949 0.897 0.854 0.845 0.803 0.736 0.715 0.704 0.698 0.698 0.697 0.697 

7 1.030 0.948 0.876 0.860 0.821 0.752 0.728 0.715 0.710 0.708 0.707 0.708 

8 1.114 1.001 0.899 0.872 0.836 0.762 0.736 0.724 0.719 0.715 0.716 0.715 

9 1.197 1.054 0.923 0.881 0.845 0.771 0.743 0.730 0.723 0.722 0.721 0.721 

10 1.279 1.106 0.942 0.888 0.854 0.777 0.748 0.736 0.729 0.725 0.725 0.724 

15 1.673 1.361 1.041 0.911 0.877 0.793 0.763 0.747 0.739 0.737 0.735 0.734 

16 1.748 1.409 1.062 0.916 0.878 0.796 0.763 0.750 0.741 0.739 0.737 0.735 

17 1.819 1.455 1.080 0.920 0.883 0.798 0.766 0.749 0.742 0.739 0.738 0.737 

18 1.891 1.499 1.097 0.923 0.884 0.800 0.767 0.753 0.743 0.739 0.739 0.738 

19 1.961 1.545 1.116 0.925 0.888 0.803 0.769 0.752 0.742 0.741 0.740 0.740 

20 2.028 1.592 1.132 0.929 0.888 0.803 0.768 0.752 0.745 0.742 0.741 0.739 

21 2.098 1.634 1.148 0.929 0.890 0.805 0.770 0.754 0.745 0.743 0.743 0.741 

22 2.164 1.675 1.167 0.933 0.892 0.804 0.771 0.756 0.746 0.744 0.740 0.743 

23 2.233 1.721 1.184 0.934 0.894 0.805 0.769 0.755 0.747 0.744 0.742 0.741 

24 2.297 1.763 1.201 0.938 0.894 0.806 0.772 0.755 0.746 0.744 0.742 0.742 

25 2.360 1.803 1.216 0.939 0.895 0.807 0.773 0.756 0.747 0.745 0.743 0.742 

30 2.678 2.006 1.298 0.948 0.898 0.809 0.775 0.758 0.746 0.745 0.744 0.744 

35 2.982 2.196 1.374 0.955 0.900 0.812 0.776 0.760 0.750 0.748 0.747 0.745 

40 3.274 2.381 1.443 0.963 0.903 0.813 0.779 0.759 0.751 0.748 0.747 0.746 

45 3.559 2.559 1.511 0.969 0.905 0.813 0.777 0.761 0.753 0.748 0.748 0.747 

50 3.833 2.733 1.579 0.974 0.907 0.814 0.780 0.763 0.754 0.750 0.748 0.748 

60 4.379 3.066 1.712 0.984 0.910 0.816 0.779 0.763 0.753 0.751 0.749 0.748 

70 4.901 3.392 1.840 0.992 0.910 0.817 0.780 0.763 0.754 0.751 0.749 0.749 

80 5.415 3.709 1.962 1.002 0.910 0.819 0.782 0.763 0.754 0.750 0.751 0.748 

90 5.917 4.019 2.079 1.011 0.911 0.818 0.783 0.765 0.755 0.752 0.750 0.751 

100 6.426 4.322 2.195 1.019 0.912 0.818 0.783 0.765 0.754 0.752 0.750 0.750 

200 11.16 7.194 3.268 1.103 0.914 0.821 0.784 0.766 0.756 0.751 0.751 0.750 

300 15.69 9.909 4.254 1.180 0.917 0.822 0.784 0.766 0.757 0.755 0.751 0.752 

400 20.10 12.53 5.194 1.256 0.917 0.823 0.785 0.766 0.757 0.754 0.751 0.752 

500 24.43 15.11 6.110 1.328 0.918 0.822 0.785 0.767 0.756 0.753 0.752 0.752 

1000 45.58 27.58 10.47 1.671 0.919 0.824 0.785 0.768 0.757 0.753 0.752 0.750 

188 
Table 6. Critical Values for K-S Test Statistic for Significance Level = 0.05 

n\k 0.010 0.025 0.050 0.10 0.20 0.50 1.0 2.0 5.0 10.0 20.0 50.0 

5 0.419 0.409 0.401 0.398 0.388 0.372 0.364 0.360 0.358 0.358 0.357 0.357 

6 0.397 0.384 0.373 0.369 0.364 0.349 0.341 0.336 0.333 0.332 0.332 0.332 

7 0.385 0.369 0.353 0.348 0.342 0.327 0.320 0.315 0.313 0.312 0.311 0.311 

8 0.372 0.354 0.336 0.329 0.323 0.309 0.301 0.297 0.295 0.294 0.294 0.293 

9 0.362 0.342 0.321 0.312 0.307 0.294 0.287 0.282 0.280 0.279 0.279 0.279 

10 0.352 0.331 0.308 0.298 0.294 0.281 0.274 0.270 0.267 0.267 0.266 0.266 

15 0.318 0.293 0.264 0.249 0.245 0.234 0.228 0.224 0.222 0.222 0.221 0.221 

16 0.313 0.288 0.258 0.242 0.238 0.227 0.221 0.218 0.216 0.215 0.215 0.214 

17 0.308 0.283 0.252 0.235 0.231 0.221 0.215 0.212 0.210 0.209 0.209 0.208 

18 0.303 0.278 0.246 0.229 0.225 0.215 0.209 0.206 0.204 0.203 0.203 0.203 

19 0.299 0.273 0.242 0.224 0.220 0.210 0.204 0.201 0.199 0.199 0.198 0.198 

20 0.295 0.269 0.237 0.218 0.214 0.205 0.199 0.196 0.194 0.194 0.193 0.193 

21 0.291 0.265 0.232 0.213 0.210 0.200 0.195 0.192 0.190 0.189 0.189 0.189 

22 0.288 0.261 0.228 0.209 0.206 0.196 0.191 0.188 0.186 0.185 0.185 0.185 

23 0.285 0.258 0.225 0.205 0.201 0.192 0.187 0.184 0.182 0.182 0.181 0.181 

24 0.281 0.255 0.221 0.201 0.197 0.188 0.183 0.180 0.178 0.178 0.178 0.177 

25 0.279 0.252 0.218 0.197 0.193 0.184 0.180 0.177 0.175 0.175 0.174 0.174 

30 0.266 0.239 0.204 0.181 0.177 0.169 0.165 0.162 0.160 0.160 0.160 0.160 

35 0.256 0.228 0.193 0.168 0.165 0.157 0.153 0.151 0.149 0.149 0.148 0.148 

40 0.248 0.220 0.183 0.158 0.154 0.148 0.144 0.141 0.140 0.139 0.139 0.139 

45 0.241 0.212 0.176 0.150 0.146 0.139 0.136 0.133 0.132 0.132 0.132 0.131 

50 0.235 0.206 0.170 0.143 0.139 0.132 0.129 0.127 0.126 0.125 0.125 0.125 

60 0.226 0.196 0.159 0.131 0.127 0.121 0.118 0.116 0.115 0.115 0.114 0.114 

70 0.218 0.189 0.151 0.122 0.118 0.113 0.110 0.108 0.107 0.106 0.106 0.106 

80 0.212 0.182 0.145 0.114 0.111 0.105 0.103 0.101 0.100 0.100 0.099 0.099 

90 0.207 0.177 0.139 0.108 0.104 0.100 0.097 0.095 0.094 0.094 0.094 0.094 

100 0.203 0.173 0.135 0.103 0.099 0.095 0.092 0.091 0.090 0.089 0.089 0.089 

200 0.179 0.149 0.110 0.075 0.070 0.067 0.065 0.064 0.064 0.064 0.064 0.063 

300 0.169 0.138 0.099 0.063 0.058 0.055 0.054 0.053 0.052 0.052 0.052 0.052 

400 0.163 0.132 0.092 0.056 0.050 0.048 0.047 0.046 0.045 0.045 0.045 0.045 

500 0.159 0.127 0.087 0.051 0.045 0.043 0.042 0.041 0.041 0.040 0.040 0.040 

1000 0.148 0.117 0.076 0.039 0.032 0.030 0.030 0.029 0.029 0.029 0.029 0.029 

189 
Table 7. Critical Values for A-D Test Statistic for Significance Level = 0.01 

n\k 0.010 0.025 0.050 0.10 0.20 0.50 1.0 2.0 5.0 10.0 20.0 50.0 

5 1.075 1.077 1.105 1.145 1.068 0.945 0.905 0.890 0.883 0.882 0.879 0.879 

6 1.195 1.156 1.142 1.183 1.121 0.990 0.946 0.928 0.918 0.916 0.911 0.912 

7 1.314 1.230 1.176 1.207 1.156 1.019 0.979 0.951 0.944 0.938 0.935 0.938 

8 1.430 1.311 1.209 1.226 1.181 1.044 0.990 0.970 0.961 0.955 0.956 0.953 

9 1.548 1.385 1.250 1.242 1.196 1.058 1.007 0.984 0.967 0.968 0.969 0.967 

10 1.658 1.464 1.281 1.249 1.214 1.071 1.018 0.994 0.981 0.977 0.975 0.973 

15 2.174 1.818 1.434 1.290 1.248 1.100 1.048 1.018 1.002 0.999 0.997 0.999 

16 2.260 1.879 1.469 1.304 1.253 1.112 1.047 1.019 1.007 1.004 1.000 0.999 

17 2.353 1.942 1.495 1.307 1.260 1.110 1.053 1.023 1.008 1.004 1.003 1.000 

18 2.441 2.004 1.521 1.317 1.260 1.116 1.054 1.027 1.015 1.006 1.005 1.003 

19 2.532 2.069 1.548 1.319 1.267 1.115 1.059 1.026 1.013 1.010 1.006 1.008 

20 2.611 2.123 1.570 1.329 1.268 1.118 1.056 1.031 1.016 1.012 1.005 1.009 

21 2.704 2.172 1.595 1.323 1.270 1.126 1.057 1.031 1.017 1.013 1.013 1.008 

22 2.780 2.226 1.619 1.339 1.279 1.119 1.062 1.036 1.023 1.014 1.011 1.013 

23 2.862 2.290 1.646 1.334 1.281 1.125 1.059 1.034 1.017 1.020 1.012 1.013 

24 2.934 2.340 1.667 1.341 1.277 1.126 1.065 1.035 1.020 1.015 1.012 1.013 

25 3.019 2.383 1.690 1.342 1.281 1.127 1.064 1.038 1.021 1.017 1.014 1.013 

30 3.393 2.634 1.800 1.365 1.286 1.133 1.072 1.044 1.023 1.023 1.019 1.018 

35 3.744 2.865 1.904 1.371 1.286 1.136 1.072 1.045 1.027 1.025 1.021 1.018 

40 4.085 3.088 1.988 1.382 1.294 1.138 1.076 1.046 1.030 1.027 1.023 1.022 

45 4.408 3.299 2.077 1.388 1.298 1.141 1.074 1.048 1.036 1.030 1.026 1.024 

50 4.734 3.500 2.162 1.407 1.304 1.142 1.079 1.053 1.034 1.029 1.028 1.025 

60 5.334 3.889 2.330 1.419 1.308 1.144 1.079 1.054 1.032 1.032 1.029 1.030 

70 5.915 4.258 2.483 1.430 1.307 1.145 1.079 1.055 1.038 1.031 1.031 1.028 

80 6.503 4.620 2.622 1.445 1.302 1.150 1.085 1.055 1.036 1.033 1.032 1.029 

90 7.050 4.960 2.759 1.458 1.312 1.149 1.086 1.056 1.038 1.034 1.031 1.033 

100 7.609 5.302 2.895 1.471 1.308 1.149 1.085 1.054 1.042 1.035 1.033 1.032 

200 12.74 8.464 4.130 1.584 1.310 1.156 1.089 1.059 1.041 1.031 1.032 1.033 

300 17.54 11.39 5.224 1.697 1.314 1.154 1.090 1.058 1.043 1.038 1.033 1.031 

400 22.18 14.18 6.252 1.793 1.321 1.158 1.093 1.057 1.043 1.039 1.035 1.034 

500 26.74 16.91 7.253 1.885 1.319 1.155 1.089 1.057 1.047 1.040 1.034 1.034 

1000 48.73 30.00 11.94 2.296 1.325 1.157 1.092 1.060 1.043 1.035 1.036 1.031 

190 
Table 8. Critical Values for K-S Test Statistic for Significance Level = 0.01 

n\k 0.010 0.025 0.050 0.10 0.20 0.50 1.0 2.0 5.0 10.0 20.0 50.0 

5 0.464 0.458 0.454 0.456 0.451 0.431 0.421 0.414 0.410 0.410 0.408 0.408 

6 0.453 0.441 0.431 0.431 0.423 0.402 0.391 0.385 0.382 0.381 0.380 0.380 

7 0.437 0.421 0.406 0.404 0.399 0.380 0.369 0.362 0.360 0.358 0.357 0.357 

8 0.423 0.407 0.388 0.384 0.379 0.360 0.349 0.344 0.340 0.339 0.339 0.338 

9 0.412 0.393 0.373 0.365 0.360 0.343 0.333 0.327 0.323 0.323 0.322 0.322 

10 0.402 0.382 0.358 0.350 0.345 0.328 0.318 0.312 0.309 0.308 0.308 0.307 

15 0.362 0.338 0.308 0.292 0.288 0.274 0.266 0.261 0.258 0.257 0.257 0.256 

16 0.356 0.331 0.301 0.284 0.280 0.266 0.258 0.253 0.251 0.250 0.249 0.249 

17 0.350 0.325 0.294 0.277 0.272 0.259 0.251 0.246 0.244 0.243 0.242 0.242 

18 0.345 0.319 0.288 0.270 0.265 0.252 0.245 0.240 0.237 0.236 0.236 0.236 

19 0.340 0.314 0.282 0.263 0.259 0.246 0.238 0.234 0.232 0.231 0.230 0.230 

20 0.335 0.309 0.276 0.257 0.253 0.240 0.233 0.228 0.226 0.225 0.225 0.225 

21 0.331 0.304 0.271 0.251 0.247 0.235 0.228 0.223 0.221 0.220 0.220 0.219 

22 0.327 0.300 0.267 0.246 0.242 0.230 0.223 0.219 0.216 0.216 0.215 0.215 

23 0.323 0.296 0.262 0.241 0.237 0.225 0.218 0.215 0.212 0.211 0.211 0.210 

24 0.318 0.292 0.258 0.236 0.232 0.221 0.214 0.210 0.208 0.207 0.207 0.206 

25 0.315 0.288 0.254 0.232 0.228 0.216 0.210 0.206 0.204 0.203 0.203 0.203 

30 0.300 0.272 0.237 0.213 0.209 0.199 0.193 0.189 0.187 0.186 0.186 0.185 

35 0.288 0.260 0.224 0.198 0.194 0.185 0.179 0.176 0.174 0.173 0.173 0.172 

40 0.278 0.249 0.213 0.187 0.182 0.173 0.168 0.165 0.163 0.162 0.162 0.162 

45 0.270 0.241 0.204 0.176 0.172 0.164 0.158 0.156 0.154 0.154 0.153 0.153 

50 0.263 0.233 0.196 0.168 0.164 0.156 0.151 0.148 0.146 0.146 0.146 0.145 

60 0.251 0.221 0.184 0.154 0.150 0.143 0.138 0.136 0.134 0.134 0.133 0.133 

70 0.242 0.212 0.174 0.144 0.139 0.132 0.128 0.126 0.124 0.124 0.124 0.124 

80 0.234 0.204 0.166 0.135 0.130 0.124 0.120 0.118 0.117 0.116 0.116 0.116 

90 0.228 0.198 0.159 0.128 0.123 0.117 0.114 0.111 0.110 0.110 0.109 0.110 

100 0.223 0.192 0.154 0.122 0.117 0.111 0.108 0.106 0.105 0.104 0.104 0.104 

200 0.194 0.163 0.124 0.089 0.083 0.079 0.077 0.075 0.074 0.074 0.074 0.074 

300 0.181 0.150 0.110 0.074 0.068 0.065 0.063 0.062 0.061 0.061 0.061 0.060 

400 0.173 0.142 0.102 0.066 0.059 0.056 0.054 0.053 0.053 0.053 0.053 0.053 

500 0.168 0.137 0.096 0.060 0.053 0.050 0.049 0.048 0.047 0.047 0.047 0.047 

1000 0.155 0.123 0.083 0.045 0.037 0.036 0.035 0.034 0.034 0.033 0.033 0.033 

191 
192 
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