PUBUCATSONS

NIST Special Publication 500-244

Information Technology:

An Interpretation of the Guide to the

Expression of Uncertainty in

Measurement

Raghu Kacker

/vio. "Sod-M

Zooo

Nisr

National Institute of Standards and Technology

Technology Administration, U.S. Department of Commerce
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NIST Special Publication 500-244

Information Technology:

An Interpretation of the Guide to the

Expression of Uncertainty in Measurement

U.S. Department of Commerce

William M. Daley, Secretary

Technology Administration

Dr. Cheryl L. Shavers, Under Secretary ofCommercefor Technology

National Institute of Standards and Technology

Raymond G. Kammer, Director

Raghu Kacker

Information Technology Laboratory

National Institute ofStandards and Technology

Gaithersburg MD 20890-8980

May 2000
Reports on Information Technology

The Information Technology Laboratory (ITL) at the National Institute of Standards and

Technology (NIST) stimulates U.S. economic growth and industrial competitiveness through technical

leadership and collaborative research in critical infrastructure technology, including tests, test methods,

reference data, and forward-looking standards, to advance the development and productive use of

information technology. To overcome barriers to usability, scalability, interoperability, and security in information systems and networks, ITL programs focus on a broad range of networking, security, and

advanced information technologies, as well as the mathematical, statistical, and computational sciences.

This Special Publication 500-series reports onlTL's research in tests and test methods for information

technology, and its collaborative activities with industry, government, and academic organizations.

Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately.

Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose.

National Institute of Standards and Technology

Special Publication 500-244

Natl. Inst. Stand. Technol.

Spec. Publ. 500-244

1 8 pages (May 2000)

CODEN: NSPUE2

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 2000

For sale by the Superintendent of Documents

U.S. Government Printing Office

Washington, DC 20402-9325
AN INTERPRETATION OF THE GUIDE TO THE EXPRESSION OF

UNCERTAINTY IN MEASUREMENT

Raghu Kacker

National Institute of Standards and Technology

100 Bureau Drive, Stop 8980

Gaithersburg, MD 20899-8980

Raghu.kacl^er@nist.gov

Phone 301-975-2109

ABSTRACT

The Guide to the Expression of Uncertainty in Measurement (GUM) is intended for all scientific and technological measurements in science, engineering, commerce, industry,

and regulation. So the GUM must have a clear interpretation. But it mixes up concepts

from frequentist and Bayesian statistics in ambiguous ways. Therefore, as presented, the

GUM is not cleai^ and liable to be applied in more than one way, leading to more than one way of expressing uncertainty in measurement. This paper attempts to present a clear

and coherent interpretation of the GUM and proposes a simple and widely applicable

approach to construct expanded uncertainty intervals. Our hope is that the clarifications

and the viewpoints presented here will promote a more consistent use of the GUM and

facilitate its application to situations not explicitly covered in the original document.

Key Words: Bayesian Analysis, Expanded Uncertainty, Frequentist Statistics, Metrology,

Statistics, Uncertainty

1. INTRODUCTION

The Guide to the Expression of Uncertainty in Measurement [1], commonly referred to as the GUM, is promulgating a standardized approach for evaluating and expressing

uncertainty in measurement, and its impact is growing. In addition to providing a standardized approach for expressing uncertainty, the GUM has provided a practical

approach for incorporating scientific judgment with the results of statistical analyses of

measurement data. Both sources of knowledge are generally needed to evaluate

uncertainty economically. Another advantage of the GUM is that the output from one

stage of measurement may be used as an input to a subsequent stage. Thus the GUM has

provided a practical way to partition a complex measurement problem into smaller, more

manageable components and to inter-link a hierarchy of measurements. The latter benefit

is useful in establishing the traceability of commercial and scientific measurements to the

national and international standards.

The GUM is intended for all scientific and technological measurements in science,

engineering, commerce, industry, and regulation. The GUM is now an "American

National Standard for Expressing Uncertainty [2]." So the GUM must have an

1
unambiguous interpretation. But even some of the most basic definitions of the GUM are not exactly clear. Consider the meaning of uncertainty. The GUM (Section 2.2.3)

defines uncertainty of measurement as a "parameter, associated with the result ofa

measurement, that characterizes the dispersion ofthe values that could reasonably be

attributed to the measurand." The GUM (Section 2.3.1) defines standard uncertainty as "uncertainty ofthe result ofa measurement expressed as a standard deviation." The

GUM (Section 2.3.5) defines expanded uncertainty as a "quantity defining an interval

about the result ofa measurement that may be expected to encompass a large fraction of

the distribution ofvalues that could reasonably be attributed to the measurand."

The definition of uncertainty may be interpreted in the following two ways. Frequentist

viewpoint: uncertainty is about the result of measurement assuming that the value of

measurand is an unknown constant ~ traditionally called the true value. Bayesian

viewpoint: uncertainty is about the value of measurand, treated as a random variable,

given that the result of measurement, the available measurement data, and scientific

judgment are loiown quantities. The phrase "uncertainty of the result of measurement" in

the definition of standard uncertainty supports the frequentist viewpoint. The frequentist

viewpoint leads to the traditional concepts of true value and error. But the GUM (Annex

D) discourages the use of these traditional concepts. In this sense, the GUM supports the

Bayesian viewpoint. But then the GUM (Annex G) motivates the use of a Student's t- distribution from the viewpoint of frequentist sampling theory to assign the coverage

probability to an interval about the result of measurement defined by expanded

uncertainty. The frequentist viewpoint leads to the concept of confidence intervals. But

the GUM (Section 6.2.2) states that the word "confidence" is not used to modify the word

"interval" when referring to the interval defined by expanded uncertainty. This is what

we mean when we say that the GUM mixes up concepts from frequentist and Bayesian

statistics in ambiguous ways.

The consequences of this mix-up include the following. First, the GUM is liable to be

applied in more than one way, leading to more than one way of expressing uncertainty in

measurement. For example, many users believe that the frequentist confidence intervals,

where the result of measurement and the standard uncertainty are treated as random

variables, agree with the GUM as do the intervals defined by expanded uncertainty,

where the value of measurand is a random variable. Second, the meaning of "being

GUM compliant" is ambiguous. Third, the user would not be sure how to apply the

GUM to situations not explicitly covered in the original document. In Section 2, we

attempt to present a clear and coherent interpretation of the GUM and propose a simple

and widely applicable approach for constructing expanded uncertainty intervals. A

summary is given in Section 3, and a number of practical comments and

recommendations are given in Section 4.

2. AN INTERPRETATION OF THE GUM

The measurand is a particular quantity subject to measurement. The object of

measurement is to determine (assess) the value ofthe measurand (the GUM, Section

3.1.1). In some cases, the measurand is defined by a particular (standard) method of

measurement. The GUM applies to measurands that are characterized by a scalar value.

2
Additional guidance is needed for measurands that are characterized by a vector or a function defined over some domain. Work is in progress to extend the GUM in this

direction [3].

Since no measurement is perfect (except when counting the elements of a small set of

discrete items), no measured quantity is known exactly (see, the GUM, Annex D). That

is, the state of knowledge about the value of a measured quantity is uncertain. There are two well-established and distinct ways of defining and quantifying uncertainty:

frequentist and Bayesian. The frequentist sampling theory assumes that the value of

measurand is an unknown constant and the result of measurement is a random variable.

A Bayesian approach treats the value of measurand as a random variable with a probability distribution representing the state of knowledge given that the result of

measurement is a knovm quantity. The results of statistical analyses based on frequentist

sampling theory are usually simpler and, for historical reasons, familiar to metrologists.

The GUM was motivated in part to incorporate scientific judgment with the results of

frequentist statistical analyses (see, the GUM, Section 0.7). So the GUM has mixed up

frequentist and Bayesian concepts and introduced a new terminology. We will show that

the GUM is clear and coherent if we adopt a Bayesian line of thinking. That is treat all quantities involved in measurement as random variables with probability distributions

representing the states of knowledge, and treat the results of frequentist statistical

analyses as approximations to the corresponding results of Bayesian analyses. Another

advantage of the proposed interpretation of the GUM is that it affords a very simple

approach for constructing expanded uncertainty intervals.

The GUM is mainly concerned with the expected values and the standard deviations of

the random variables involved in measurement rather than with the fully characterized

probability distribytions. The reason, we believe, is that it is easier to estimate or assess the expected value and the standard deviation of a random variable than judge the

complete probability distribution. The expected value and the standard deviation of a random variable are said to characterize its probability distribution. Since Bayesian

methods work with the probability distributions of the involved variables, the GUM is not

intended to be a completely Bayesian approach in our view. Another researcher has

shovm that the recommendations of the GUM can be regarded as approximate solutions

to certain frequentist and Bayesian inference problems [4].

The GUM is based on the concept of measurement equation. A measurement equation is a functional relationship that expresses the value of measurand as a function of all those

variables that affect its assessment. The expected value and the standard deviation of an

input variable to the measurement equation are evaluated fi"om statistical analysis of

measurement data and/or by scientific judgment. The method of evaluation is referred to as Type A evaluation when measurement data are used, and Type B evaluation when

scientific judgment is used. These two modes of evaluation are not necessarily mutually

exclusive [3]. The evaluated expected values and the standard deviations of the input

variables are then combined through the measurement equation to obtain the expected

value and the standard deviation of the value of measurand. The expected value is taken

3
as the estimated value of measurand and the standard deviation as the combined standard

uncertainty concerning the value of measurand.

2.1 Type A and Type B Evaluations ofStandard Uncertainty

The statistical methods employed for Type A evaluation may be either Bayesian or

frequentist. But for simplicity, Type A evaluations are usually frequentist estimates. We

will briefly describe the two approaches. Type A evaluations from Bayesian analyses

and Type B evaluations from scientific judgment are mathematically compatible inputs to the measurement equation because both treat the input quantities as random variables.

But Type A evaluations from frequentist analyses are not mathematically compatible

with Type B evaluations, because the frequentist methods treat the input quantities as unknown constants. We will illustrate that, in the practical cases of interest, the

frequentist estimates may be regarded as approximations to the corresponding results

from Bayesian analyses based on non-informative prior distributions. Therefore, it is legitimate to treat frequentist estimates and Type B assessments as mathematically

compatible inputs to the measurement equation.

Suppose the value of the quantity of interest is X. A Bayesian analysis starts with a prior

probability distribution representing the state of knowledge about X before measurement.

The expected value, the variance, and the standard deviation (square root of variance) of

the prior distribution are called prior expected value, prior variance, and prior standard

deviation, and denoted by E(X), V(X), and SD(X) respectively. The relationship between

the value of X and the statistical measurement data is expressed by a "likelihood

function." Generally, both Bayesians and frequentists agree on the likelihood function.

The prior distribution and the likelihood function are then combined by Bayes theorem

[5] to obtain a posterior distribution representing the state of knowledge about X after

measurement. The expected value, the variance, and the standard deviation of the

posterior distribution are called posterior expected value, posterior variance, and posterior

standard deviation, and denoted by E(X | data), V(X | data), and SD(X | data)

respectively. This notation indicates that the posterior distribution is conditional on the

data. The posterior distribution can be used as a prior distribution in a subsequent

measurement of the same quantity, and the process can be repeated any number of times.

The posterior expected value E(X | data) is taken as an estimate of X, and SD(X | data) is taken as a measure of the uncertainty concerning X after measurement.

In a frequentist analysis, the value of the quantity of interest X is treated as an unknown

constant ~ traditionally called the true value. The output of a frequentist statistical

analysis is an estimate of X and an estimated standard deviation of the estimate.

Consider the simple case where X is estimated from a sample (set) of n measurements

that are assumed to be independent and identically normally distributed random variables

with expected value X and some variance a . Let x and s denote the sample mean and

the sample variance of the n measurements. Then x, s , and s are the estimates of X, ct , and a respectively. The probability distribution of x, called a sampling distribution, is also normal but with expected value X and variance a^/n. The ratio s/Vn is an estimate

a/Vn. The standard deviation a/Vn, called population standard deviation of the mean.

4
characterizes the tightness of the sanipling distribution of x about E(x) = X. So s/Vn is an

estimate of the tightness of the sampHng distribution of x about X. Thus s/Vn, called

sample standard deviation of the mean, is a measure of the doubt about x as an estimate

ofX.

The frequentist estimates x and s/Vn may be viewed as approximations to the Bayesian

posterior expected value E(X | data) and the standard deviation SD(X | data) respectively

based on a class on prior distributions called non-informative prior distributions [5]. A

non-informative prior distribution represents the situation that relatively little is known a

priori about the value X of the quantity of interest in advance of measurement. It can be

shown that the Bayesian posterior expected value and variance based on non-informative

prior distributions are approximately equal to the corresponding estimates from

frequentist sampling theory, provided the number of independent measurements on which

the estimates are based is not too small [5]. This assertion is illustrated in the Appendix.

Note: In the case of n independent and identically normally distributed measurements

with mean x and standard deviation s, the Bayesian posterior distribution of (X -

x)/(s/Vn), based on a pair of common non-informative prior distributions, is the t- distribution with (n -1) degrees of freedom [5]. Thus SD(X | data) = V[(n - l)/(n - 3)] x

(s/Vn), which is defined only when n is four or more. Therefore, at least four independent

measurements are required to claim that the frequentist estimate s/Vn approximates the

Bayesian posterior standard deviation SD(X | data).

Frequently, the data structures and the statistical models underlying the frequentist

analyses are more complicated than the simple example of a series of independent and

identically normally distributed measurements discussed above. The outputs of the data

analysis are, nonetheless, an estimate of a parameter and an estimated standard deviation

of the estimate. Even with more complicated analyses, in the practical cases of interest,

the frequentist estimates may be regarded as approximations of the Bayesian posterior

expected value and standard deviation corresponding to some (proper or improper) noninformative prior distributions [5]. This relationship between the frequentist and the

Bayesian results enables us to interpret the GUM from a Bayesian line of thinlcing and

still employ frequentist statistics for Type A evaluations.

In a Type B evaluation, scientific judgment is expressed in terms of a fully characterized

probability distribution for X. Thus the expected value and the variance of X are specified values. The GUM treats Type B evaluations of the expected value and the

variance in exactly the same way as it treats Type A evaluations. One should not belabor

the distinction between the two modes of evaluation [3]. We need a general notation for

the expected value, the variance, and the standard deviation of an input variable

regardless of the mode of evaluation. We will denote the current state of knowledge

about the expected value, the variance, and the standard deviation of an input variable X

based on all available information as E(X [ .), V(X | .), and SD(X | .) respectively. The

expected value E(X | .), denoted by x, is taken as the estimated value of X and the

standard deviation SD(X | .), denoted by u(x), is referred to as the standard uncertainty

concerning X. The variance V(X | .) is equal to u (x).

5
2.2 Measurement Equation

Let Y denote the value of measurand, treated as a random variable with a probability

distribution representing the state of knowledge. In the GUM paradigm, the primary

object of measurement is to evaluate the expected value and the standard deviation of the

value of measurand Y from all available measurement data and scientific judgment.

Following the notation of Subsection 2.1, we will denote the expected value, the

variance, and the standard deviation of Y as E(Y | .), V(Y | .), and SD(Y | .) respectively.

The GUM is concerned with applications where E(Y | .) and SD(Y | .) are determined

from the expected values and the standard deviations of some number N of input

variables Xi, X2, Xn through a functional relationship, denoted by f, and called the

measurement equation:

Y = f(X,,X2,...,XJ. (1)

In a broad sense, the measurement equation represents the procedure used to determine

the value of measurand. Some of the input variables Xj may themselves be viewed as measurands and functions of additional input variables. Therefore, the measurement

equation provides a practical way to partition a complex measurement problem into

smaller more manageable components and to inter-link a hierarchy of measurements. In

some cases, the function f is expressed as a system of equations. In some other cases, the

function f may be the identity function Y = X or may be expressed asY = X + Ci + C2 +

... + Cm, where Ci, C2, Cm, are correction for systematic (non-random) effects. The

function f may be determined experimentally or may exist only as an algorithm that is evaluated numerically.

The expected value E(Xi | .), the variance V(Xi | .), and the standard deviation SD(Xi | .) of an input variable Xj for i 1 , . .

. , N may be estimated from measurement data (Type

A) and/or assessed by scientific judgment (Type B). Therefore, the measurement

equation provides a practical way to combine scientific judgment and the results of

statistical analyses of measurement data. The expected value E(Y | .) is obtained by

substituting the expected values E(Xj | .) for the input variables Xj for i = 1 , . .

. , N in the

measurement equation:

E(Y I .) = f (E(X, 1 .),E(X2 1 .),-,E(X^ I .)). (2) In order to determine V(Y | .), the measurement equation is approximated by a first-order

Taylor series. This provides the following equation called the law of propagation of

uncertainty:

V(Y|.) = Xc,Mx,|.) + 2m;c,c^SD(X,|.)SD(X.|.)r(X,,Xp, (3)

i=i i=i j=i+i where Cj represents the partial derivative of the function f with respect to X-, evaluated at E(Xi), and r(Xi, Xj) denotes the correlation coefficient between Xj and Xj for i, j = 1 , 2,

. . ., N. The GUM (Section F. 1 .2) describes a number of approaches to quantify

correlation coefficient. As discussed in the GUM (Section 5.1.2, Note), equation (3) may

be expanded to include higher order terms from the Taylor series. Then, SD(Y | .) is V(V(Y I .)). The method of evaluating the expected value E(Y | .) and the standard

deviation SD(Y | .) from equations (2) and (3) respectively is referred to as the method of

6
propagating uncertainties. The effectiveness of this method depends on the thoroughness

of the measurement equation and the adequacy of the expected values and the standard

deviations of the input variables. An alternative to propagating uncertainties is indicted

later in this section. The estimated value of measurand, denoted by y, is the expected

value E(Y | .), and the standard uncertainty concerning the value of measurand, denoted

by u(y), is the standard deviation SD(Y | .). The estimated value E(Y | .) = y is also

referred to as the result ofmeasurement. The variance V(Y | .) is equal to u^(y). The

quantities y and u(y) represent the current state of Icnowledge about the expected value

and the standard deviation of Y based on all available information. According to this

interpretation, any probability distribution that has the expected value y and the standard

deviation u(y) qualifies as a state-of-knowledge distribution of Y. Thus standard

uncertainty is the standard deviation of a state-of-knowledge distribution of the value of

measurand. A probability distribution characterized by y and u(y) is not necessarily the

same as a mathematically derived probability distribution of Y. Note that equation (3)

propagates uncertainties rather than distributions. When it is useful to indicate that u(y)

has been obtained by combining a number of uncertainty components, the standard

uncertainty is termed combined standard uncertainty, and denoted by Uc(y).

An alternative to the method of propagating uncertainties is numerical simulation.

Numerical simulation avoids approximating the function f, of equation (1), by a Taylor

series. Simulation is possible whenever the measurement equation (1) can be

numerically evaluated. Using assumed or derived forms for the probability distributions

characterized by the expected values and the standard deviations of the input variables, a

sufficient number of the values of Y may be simulated numerically. The simulated

values of Y then provide E(Y | .) = y and SD(Y | .) = Uc(y). Numerical simulation is a

legitimate approach because the probability distributions of all input variables are fully

characterized. This approach may be referred to as a propagation of distributions by

numerical simulation rather than a propagation of uncertainties. Work is progressing in

this direction [3].

Note: Suppose extensive experimental, scientific, and theoretical knowledge exists to afford a fully Bayesian approach to determine the (posterior) probability distribution of

the value of measurand. In that case one may use a fully Bayesian approach. The results

would be "GUM compliant" with the identity function Y = X as measurement equation.

2.3 Expanded Uncertainty, Coverage Factor, and Coverage Probability

In certain applications, it is necessary to express the uncertainty as an interval about the

estimated value of measurand. The GUM concepts of expanded uncertainty, coverage

factor, and coverage probability relate to this need. We will interpret these concepts from

the viewpoint of treating the value of measurand as a random variable. The expanded

uncertainty, denoted by U, is obtained by multiplying the standard uncertainty SD(Y | .) = Uc(y) by a factor denoted by k. Thus U = k x SD(Y | .). Expanded uncertainty defines

the interval [E(Y | .) - k x SD(Y | .), E(Y | .) + k x SD(Y | .)] about E(Y | .) = y. The

GUM has not assigned a name to this interval. We will call this interval an expanded

uncertainty interval and write it as [E(Y | .) ± k x SD(Y | .)] = [y ± k x Uc(y)]. This

7
interval may alternatively be referred to as a k-standard uncertainty interval. The

coverage probability associated with the expanded uncertainty interval is the probability

Pr[E(Y I .) - k X SD(Y | .) < Y < E(Y | .) + k x SD(Y | .)], where Y is a random variable,

and E(Y | .) y-> SD(Y | .) = Uc(y), and k are treated as constants. The coverage

probability concerns a state-of-knowledge distribution of Y, and it is a conditional

statement given that the evaluated expected value y and the evaluated standard

uncertainty Uc(y) are known quantities. The multiple k determines the width of the

interval and thus the coverage probability. Hence k is called a coverage factor. In order

to establish a relationship between the coverage factor and the coverage probability, some

assumption about the form of the state-of-knowledge distribution of Y is required. The

relationship between the coverage probability and the coverage factor is indicated in the

GUM by writing the latter as kp where p is coverage probability.

Note: The GUM (Section 6.2.2) uses the words "level of confidence" as a synonym for

"coverage probability." Since the term level of confidence is usually associated with

frequentist confidence intervals, we do not recommend its use in cormection with

expanded uncertainty intervals.

2.4 Doubt About Evaluated Combined Standard Uncertainty

The evaluated combined standard uncertainty Uc(y) could be doubtful for a number of

reasons. In order to assure that Uc(y) is adequate for the needs, all of the following

sources of doubt must be considered. Only a small number of independent measurements

were used in a Type A evaluation. The GUM (Section E.4.3, Table E.l) shows that the

doubt about a Type A standard uncertainty arising from purely statistical reason of

limited sampling can be surprisingly large when the number of independent

measurements is small. Likewise, Uc(y) could be doubtful because a Type B assessment

is not very reliable. Frequently, the main source of doubt is the inadequate effort made to identify significant influence quantities and the failure to include in Uc(y) the

corresponding components of uncertainty. Some influence quantities may be deemed to be significant, but the corresponding components of uncertainty cannot be assessed for

lack of sufficient experimental or scientific knowledge. The law of propagation of

uncertainty could itself be an important source of doubt about Uc(y). Use of second order

terms as discussed in the GUM (Section 5.1.2, Note) is a helpful step in the right

direction. But how does one loiow the importance of second order terms in advance of

actually computing them? Also, Uc(y) may be doubtful because the measurements may

not be independent and representative for the intended scope of the measurement

environment (see. Subsection 2.7). The quantity actually measured may be an

approximation of the quantity whose value is desired. In such cases, the discrepancy

between the intended measurand and the quantity realized for measurement could be an

important source of doubt about Uc(y). Inadequate specification of the measurand could

be an important source of doubt (see, the GUM, Section D.6.2). In addition, the doubt

about Uc(y) due to unrecognized effects could be important. Presence of such effects is suggested by significant differences in the estimated values of a common measurand by

two or more methods (or laboratories). In general, the doubt about evaluated combined

standard uncertainty Uc(y) cannot be quantified.

8
2.5 Use ofa Student's t-Distribution

Often, metrologists associate coverage factors of 2 and 3 with approximate 95 % and 99

% coverage probabilities respectively. This relationship between the coverage factor and

the coverage probability presumes an approximate normal distribution for the value of

measurand. The GUM prescribes an alternative to the normal distribution that accounts

for the doubt about standard uncertainty Uc(y) due to the small number of independent

measurements used in Type A evaluations and/or the poor reliability of Type B

assessments. The GUM prescription involves the use of a Student's t-distribution with

effective degrees of freedom as determined by the Welch-Satterthwaite approximation.

We will discuss the advantage of a t-distribution and the applicability of the GUM

prescription. The t-distribution is named after its developer W. S. Gosset, who wrote

under the pen name Student.

First, consider the special case where the value of measurand Y, treated as an unknown

constant, is estimated from a frequentist analysis of a series of n measurements that are assumed to be independent and identically normally distributed with expected value Y

and some standard deviation a. Suppose the sample mean and sample standard deviation

are y and s respectively. Then y is an estimate of Y and s/Vn is an estimate of the

population standard deviation of the mean c/Vn. It can be shown that the ratio (y - Y)/(s/Vn) has the Student's t-distribution with v = (n - 1) degrees of freedom (d.f.) [6].

Consequently, Pr[y - tp(v) x s/Vn < Y < y + tp(v) x s/Vn] = p, where tp(v) denotes a value

of the t-distribution with d.f. v = (n - 1) such that Pr[-tp( v) < t < tp(v)] = p. The interval [y

± tp(v) X s/Vn] is called a confidence interval with confidence level p. In this confidence

interval, y and s/Vn are random variables and Y is an unloiown constant. A confidence

interval is not an expanded uncertainty interval because in the latter case Y is a random

variable, and y and s/Vn are constants. It turns out that in this particular case, a Bayesian

interval exists that is numerically identical to the corresponding confidence interval. This

result comes from the following Theorem [5].

Theorem 1 : Let the sample quantities y and s be independently distributed as normal

N(Y, a In) with expected value Y and variance a In, and (a /v) times chi-square x (v)

distribution with v degrees of freedom respectively. Suppose a priori that Y and log ct are approximately independent and locally uniform. Then, given y and s , (a) a is distributed as (Vv x s) times X '(v) distribution, (b) condifional on a, Y is distributed as N(y, a^/n), and (c) unconditionally, (Y - y)/(s/Vn) has the Student's t-distribution with v =

(n - 1 ) degrees of freedom.

The prior distributions stipulated in this theorem are non-informative. From this theorem,

it follows that Pr[y - tp(v) x s/Vn < Y < y + tp(v) x s/Vn] = p, where Y is a random

variable, and y and s/Vn are constants. Thus the interval [y ± kp x u(y)], where u(y) =

s/Vn and kp = tp(v) qualifies as an expanded uncertainty interval with coverage probability

p. Thus, in the special case of independent and identically normally distributed

measurements, a frequentist confidence interval is numerically identical to the

corresponding expanded uncertainty interval.

9
The use of a t-distribiition in place of the normal distribution accounts for the doubt about

s /n as represented by the degrees of freedom v = (n - 1 ) by increasing the coverage factor

kp and hence the width of the interval [y ± kp x s/Vn] for a fixed coverage probability.

For example, suppose n = 4 and d.f. v = (n - 1) = 3. Now suppose the coverage

probability is fixed at p = 95 %, then the coverage factors for the t-distribution and the

normal distribution are to.95(3) = 3.18 and ko.gs = 1.96 respectively, a difference of 62 %.

For V of 1 5 or more the difference between the coverage factors for the t-distribution and

the normal distribution is less than 9 %, when the intended coverage probability is 95 %.

This illustrates that a t-distribution is useful when Y is estimated from a small number of

measurements that are believed, based on experimental and theoretical knowledge, to be

approximately independent and identically normally distributed. But the benefit of using

a t-distribution rather than the normal distribution is insignificant when the number of

independent measurements is more than 15.

The GUM (Section G.6.4) prescription for using a Student's t-distribution is as follows.

Evaluate the expected value E(Y | .) = y, and the combined standard uncertainty SD(Y | .) = Uc(y) from equations (2) and (3) respectively. Estimate the effective degrees of

freedom Veff of Uc(y) from the Welch-Satterthwaite approximation as discussed in the

GUM (Section G.4). Obtain tp(VetT) for the required coverage probability p from a table

of Student's t-distribution. Take kp = tp(Veff) and calculate the expanded uncertainty U =

kpxuc(y).

The concept of degrees of freedom as used by the GUM (Section E.4.3) for independent

and identically normally distributed measurements has been extended by the GUM

(Section G.4. 2) for the "reliability" of Type B evaluations. This extension has been

developed to enable the use of Welch-Satterthwaite approximation for both Type A and

Type B evaluations of the standard uncertainties. The Welch-Satterthwaite

approximation applies to those input variables X], X2, . . ., Xn that are not mutually

coiTelated.

The GUM prescription may be argued as an approximation when the measurement

equation is a linear function of N independent variables X\, X2, . . ., Xn, and Xj is estimated from a series of n\ measurements that are assumed to be independent and

identically normally distributed for every i = 1,2, . . ., N. Research is needed to understand the reasonableness of this approximation. The GUM prescription may not be

a reasonable approximation when not all of Xi, X2, . . ., Xn are estimated from a series of

independent and identically normally distributed measurements or some of the Xi, X2,

. . ., Xn are correlated or the measurement equation is a highly non-linear function of Xi,

X2, . . ., Xn. Conclusion: the GUM prescription may not be a reasonable approximation in

many applications.

10
2.6 Expanded Uncertainty Intervals Based on the Chebyshev and the Gauss

Inequalities

Some assumption about the form of the distribution of Y, as characterized by the result of

measurement y and the standard uncertainty Uc(y), is required to relate the coverage

probability and the coverage factor used to define an expanded uncertainty interval. The

coverage probability associated with an expanded uncertainty interval is doubtful to the

extent that the assumed form of the distribution of Y is doubtful. Therefore, we propose

that the metrologist report the minimum coverage probability for a class of probability

distributions rather than a specified coverage probability for a particular assumed

distribution. We will describe a simple and widely applicable approach to set the

coverage factor that defines an expanded uncertainty interval with a desired minimum

coverage probability for two common classes of distributions.

When nothing can be assumed about the distribution of Y except that E(Y | .) y and

SD(Y I .) = Uc(y), then the Chebyshev inequality [6] applies. Accordingly, Pr[y - k x

Uc(y) < Y < y + k X Uc(y)] > (1 - 1/k ). In particular, the coverage probability associated

with the expanded uncertainty interval [y ± k x Uc(y)] is at least 75 % for k = 2, and is at least 89 % for k = 3. Suppose the desired minimum coverage probability is 85 %, then

by setting 1 - 1/k^ = 0.85, we get the coverage factor k as 2.58.

In many applications, it is reasonable to assume that the distribution of Y, as characterized by E(Y | .) = y and SD(Y | .) = Uc(y), is symmetric and unimodal about y. With this assumption, we can invoke the Gauss inequality [6], and claim that Pr[y - k x

Uc(y) < Y < y + k X Uc(y)] > [1 - 4/(9k^)]. In particular, the coverage probability

associated with the expanded uncertainty interval [y ± k x Uc(y)] is at least 89 % for k =

2, and is at least 95 % for k = 3, when the distribution of Y is symmetric and unimodal

about y. Suppose the desired minimum coverage probability is 90 %, then by setting 1 - 4/(9k ) = 0.90, we get the coverage factor k as 2.1 1.

Note: A t-distribution is symmetric and unimodal. But the coverage probability

associated with the interval [y ± k x s/Vn] for k = 2, based on the t-distribution with v =

(n -1) = 3 degrees of freedom, is 86 % rather than 89 % or more. This is because the

standard deviation of the t-distribution with v degrees of freedom is a/[v/(v - 2)]. When

the degrees of freedom v = (n -1) = 3, SD(Y | .) = V3 x s/Vn = 1.732 x s/Vn. Hence s/Vn

is less than SD(Y | .). The minimum coverage probability of 89 %, associated with the

interval [y ± k x s/Vn] for k = 2, applies to symmetric and unimodal distributions that are characterized by the expected value E(Y | .) = Y and the standard deviation SD(Y | .) =

s/Vn.

2. 7Additional Comments

The choice of coverage factor k involves a trade-off between the width of the expanded

uncertainty interval [y ± k x Uc(y)] and the corresponding coverage probability for the

assumed probability distribution of Y. In the practical cases of interest, narrower

intervals corresponding to smaller values of k are more interesting. But they have lower

11
coverage probabilities. The choice of coverage factor k-2 provides a reasonable

balance between the width of the interval and the coverage probability for the commonly

assumed forms of distributions. The coverage probability associated with a 2-standard

uncertainty interval is at least 75 % regardless of the form of the distribution of Y

characterized by the result of measurement and the standard uncertainty. The coverage

probability jumps to at least 89 % when the distribution can be assumed to be symmetric

and unimodal.

One of the most critical assumptions in statistical analyses is the independence of

measurements. Suppose, for example, the intended scope of the measurement

environment is long-term involving a number of influence quantities that may not change

appreciably over short periods of time. Now suppose the available data are short-term

measurements during which a number of important influence quantities remained

constant. Then the short-term measurements could be positively correlated resulting in

under-evaluation of long-term uncertainty. So it is important to clarify the intended

scope of the measurement environment. Then the measurement protocol should be

designed to assure that the measurement data represent variation in all relevant significant

influence quantities, and that the data conform to the assumption of independence built in

the statistical model used for data analysis.

3. SUMMARY

We have shown that the GUM is clear and coherent when interpreted with the following

precepts. First, all quantities involved in measurement are random variables with

probability distributions that represent the state of knowledge about them, a la Bayesian

statistics. Second, the GUM is mainly concerned with the expected values and the

standard deviations of the random variables involved in measurement rather than with the

fully characterized probability distributions. Third, Type A estimates obtained from

frequentist analyses of measurement data are regarded as approximations to the

corresponding results from Bayesian analyses based on non-informative prior

distributions.

The GUM is based on the concept of measurement equation. A measurement equation

expresses the value of measurand as a function of all those variables that affect its assessment. The expected value and the standard deviation of an input variable to the

measurement equation are evaluated from statistical analysis of measurement data (Type

A) and/or by scientific judgment (Type B). The statistical methods employed for Type A

evaluation may be either Bayesian or frequentist. Type A evaluations from Bayesian

analyses of measurement data and Type B evaluations from scientific judgment are mathematically compatible inputs to the measurement equation because both treat the

input quantities as random variables. But Type A evaluations are usually frequentist

estimates. They are not mathematically compatible with Type B evaluations because the

frequentist methods treat the input quantities as unlmown constants. However, in the

practical cases of interest, the frequentist estimates may be regarded as approximations to the corresponding results from Bayesian analyses based on non-informative prior

distributions. Therefore, it is legitimate to treat frequentist estimates and Type B

evaluations as mathematically compatible inputs to the measurement equation. The

12
evaluated expected values and the standard deviations of the input variables are then

combined by the method of propagating uncertainties, as discussed in Subsection 2.2, to obtain the expected value and the standard deviation of the value of measurand. The

expected value is taken as the estimated value of measurand and the standard deviation is taken as the combined standard uncertainty concerning the value of measurand. The

estimated value of measurand is also referred to as the result of measurement. Any

probability distribution whose parameters match the expected value and the standard

deviation of the value of measurand qualifies as a state-of-knowledge probability

distribution of Y. Numerical simulation is an alternative to the method of propagating

uncertainties. Indeed, simulation may be a preferred approach when the measurement

equation can be numerically evaluated. The expanded uncertainty is a multiple of the

standard uncertainty that defines an interval about the estimated value of measurand that

is presumed to cover a large fraction of the distribution of Y. The multiple is called

coverage factor and the fraction of distribution covered is called coverage probability.

The coverage probability associated with an expanded uncertainty interval is a conditional statement given that the evaluated expected value and the evaluated standard

deviation of the value of measurand are Imown quantities. The GUM prescription to construct expanded uncertainty intervals, involving the use of a Student's t-distribution

with effective degrees of freedom as determined by the Welch-Satterthwaite

approximation, may not be a reasonable approximation in many applications.

Some assumption about the form of the distribution of Y, as characterized by the result of

measurement y and the standard uncertainty Uc(y), is required to relate the coverage

probability and the coverage factor used to define an expanded uncertainty interval. The

coverage probability associated with an expanded uncertainty interval is doubtful to the

extent that the assumed form of the distribution of Y is doubtful. Therefore, we have

proposed the use of the Chebyshev and the Gauss inequalities to construct expanded

uncertainty intervals with a minimum coverage probability for a class of probability

distributions.

4. COMMENTS AND RECOMMENDATIONS

An effective approach to quantify uncertainty is to make an "uncertainty budget" that

includes the important components of uncertainty and identifies their interrelationships.

Then, have the uncertainty budget reviewed by peer subject matter experts to assure that no potentially significant sources of uncertainty have been ignored, within the limits of

available knowledge, and that the estimates of the components of uncertainty seem

reasonable. Usually, the combined standard uncertainty is reported to at most two

significant digits. The components of uncertainty that contribute only a small fraction to the combined standard uncertainty are often identified in the budget as insignificant and

neglected.

Clarify the intended scope of the measurement environment for the specified measurand.

Is it short-term or long-term? Then make sure that the measurement data represent

variation in all significant influence quantities for the intended scope of measurements.

13
When using a series of measurements to estimate the value of measurand, demonstrate

that the measurement process is in a state of statistical control. The number of

independent measurements used for each component of uncertainty should be as-large-aspractical but not less than four. (Estimates based on fewer than four measurements may

be used when they are believed to be reliable based on scientific judgment and prior

experience.)

For archives of measurement data, tabulate standard uncertainties with comments rather

than expanded uncertainty intervals because it is standard uncertainties rather than

expanded uncertainty intervals that get propagated through a hierarchy of measurements.

A frequentist confidence interval is not an expanded uncertainty interval because in the

latter case the value of measurand is a random variable, and the result of measurement

and the standard uncertainty are known quantities.

The degrees of freedom, as evaluated by the Welch-Satterthwaite approximation, may not

be an adequate measure of the doubt about evaluated combined standard uncertainty.

Reason: the important sources of doubt may not be limited to the small number of

independent measurements used in Type A evaluations and/or the poor reliability of Type

B evaluations.

As a general rule, use the coverage factor two to construct expanded uncertainty

intervals. The choice of coverage factor requires some assumption about the form of the

distribution of the value of measurement, and involves a trade-off between the width of

the expanded uncertainty interval and the coverage probability. The coverage factor two

provides a reasonable balance between the width of the interval and the coverage

probability for the commonly assumed forms of distributions.

Use the Gauss inequality to set the coverage factor for a desired minimum coverage

probability when the distribution of the value of measurand, as characterized by the result

of measurement and the standard uncertainty, can be assumed to be symmetric and

unimodal.

When a particular probability distribution, such as the normal or a t-distribution, is used

to set the coverage factor for a desired coverage probability, provide some justification

that the assumed form of distribution is reasonable. The justification may have

experimental and/or theoretical basis.

Quantification of uncertainty requires expenditure of cost and time. The effort expended

must be proportional to the quality of uncertainty statement that is need by the potential users of the result of measurement.

We are interested in receiving feedback from the users of the GUM about the viewpoints

expressed in this paper.

14
ACKNOWLEDGMENT

The suggestions received from Norm Berk, Raju Datla, Tyler Estler, Keith Eberhardt,

Charles Hagwood, Gail Hixenbaugh, Al Jones, Steve Phillips, Andrew Rukhin, and Barry

Taylor are gratefully aclcnowledged.

REFERENCES

[1] Guide to the Expression of Uncertainty in Measurement (ISBN 92-67-10188-9),

International Organization for Standardization, Case postale 56, CH-121 1, Geneva 20,

Switzerland, 1995.

[2] American National Standardfor Expressing Uncertainty - U.S. Guide to the

Expression of Uncertainty in Measurement (ANSI/NCSL Z540-2-1997), National

Conference of Standards Laboratories. 1800 30^'' Street, Suite 305B, Boulder, CO 80301,

1997.

[3] Barry N. Taylor: Personal Communication, March 2000.

[4] Gleser, Leon. J., Assessing Uncertainty in Measurement, Statistical Science, 1998, 13,

pp. 277-290.

[5] Box, George E. P., and Tiao, George C, Bayesian Inference in Statistical Analysis,

Addison -Wesley Publishing Company, Reading, MA, 1973. (Theorem 2.4.1, and other

parts of the book.)

[6] Stuart, Alan, and Ord, J. Keith, Kendall's Advanced Theory ofStatistics, Distribution

Theory, Vol. 1, Oxford University Press, NY, 1987.

APPENDIX

The following example illustrates the concept of non-informative prior distributions, and

shows that the expected value of a Bayesian posterior distribution based on a noninformative prior distribution is approximately equal to the corresponding estimate from

frequentist analysis. Suppose the measurand is the mean breaking strength X of a large

batch of certain parts. Suppose a random sample (set) of n = 12 parts is selected from the

batch, and their mean breaking strength is determined to be x based on destructive

testing. Suppose the standard deviation of each measurement, including the test and the

part variation, is known with high reliability to be a = 17.3 units. For simplicity, we are assuming that the standard deviation a is known. We will assume that the sampling

distribution of x can be taken as normal with expected value X and standard deviation

a/Vn = 17.3/Vl2 = 5.0 units. Now suppose the value of sample mean x is 70.0 units.

Then the frequentist estimate of X is 70.0 units with a standard deviation 5.0 units.

A Bayesian analysis starts with a prior distribution, representing prior state of knowledge,

about X, then updates the state of knowledge based on the results of measurement.

Suppose that, based on prior laiowledge of the manufacturing process, the prior

15
distribution of X can be assumed to be normal with some expected value ]io and some

standard deviation gq. Since the probability distribution of the mean x is assumed to be

normal with expected value X and standard deviation a/Vn = 5.0 units, the probability

density function p(x | X) of x given X and a/Vn is proportional to exp[-(n/2)((x - X)/ct)^],

where n = 12, and a = 17.3 units. Now given x = 70.0 units, the probability function p(x | X) may be regarded as a function not of x but of X. When so regarded the function p(x | X) is called a likelihood function of X given x and denoted by 1(X | x). Thus

1(X I X) oc exp[~(n / 2)((x - X) / a)' ], (4)

where x = 70.0 units, n = 12, and a = 17.3 units. Then by Bayes theorem [5], the

posterior distribution of X given x is also normal with expected value E(X | x) and

standard deviation SD(X | x) where

E(X|x) = [l/(l + r)]x + [l-l/(l + r)K. (5)

SD(X|x) = (a/Vii")x(l/7r+0), . (6)

and

r = (aVn)/aJ, (7)

is the ratio of the variance of the sampling distribution of x to the prior variance of X.

The ratio r represents the importance of the prior distribution relative to the current

measurement data. Clearly as r tends to 0, E(X | x) tends to x, where x is the frequentist

estimate of X, and SD(X | x) tends to a/Vn, the standard deviation of the sampling

distribution of x. The ratio r is close to zero when the prior variance ctq^ is very large

relative to a^/n (or the sample size n is extremely large). Such values of r represent the

situation that the prior state of loiowledge is meager in relation to the information in the

current measurement data. Prior distributions for which r is close to zero are appropriately called non-informative prior distributions.

Consider two different prior distributions. Prior distribution 1 is normal N(|j.o, ctq ) with

\xo = 60.0 and ao = 10.0. Prior distribution 2 is normal N()j,o, ao ) with [Xq = 60.0 and gq =

1000.0. For prior distribution 1, r = 0.25. Thus E(X | x) = 68.0 and SD(X | x) = 4.47.

The posterior expected value E(X | x) = 68.0 is closer to the sample mean x = 70.0 than

the prior expected value [Xq = 60.0. Such is often the case, because in many scientific

applications the ratio r is small. For prior distribution 2, r = 0.000025 indicating that the

prior distribution 2 is non-informative relative to the information in the current

measurement data. In this case E(X | x) = 70.0, the same result as obtained from

frequentist analysis.

Dated: Thursday, May 25, 2000

Place: NIST, Gaithersburg, MD 20899-8980

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