Document ID: chunk:federal_register_of_legislation:F2013C00288:reg:1850:p76
Version: federal_register_of_legislation:F2013C00288
Segment Type: reg
Provision Reference: reg 1850 (pt 76/117)
Character Range: 561548–564503

data are generally described as 'non-detects' rather than 'zero' or 'not present' and the appropriate limit of detection for the analytical procedure should be reported. Data that includes both numerical data and 'non-detect' results is referred to as censored data in statistical literature.

Where the approximate percentage of non-detects is less than 15% of the relevant data set, then substitution of the LOD/2 or the LOD may be satisfactory, depending on the purpose of the analysis (US EPA 2006b). More detailed adjustments may be appropriate for where more than 15% of the relevant data set is below detection limits. In addition, sample size influences which procedure should be used to evaluate data. For example, the case where 1 sample out of 4 is not detected should be treated differently from the case where 25 samples out of 100 are non-detects. Further information can be found in US EPA (2006b) and US EPA (2007a).

Although substitution methods (such as replacing with the LOD/2 or the LOD) are reported widely in the literature for analysing data with non-detects, these approaches result in bias of the summary statistics calculated from the adjusted data set. The US EPA ProUCL software package provides alternative methods for calculating summary statistics such as the mean which do not rely on substitution methods (US EPA 2007a).

    13.2.3      Outliers
Adapted from US EPA (2006b) and BC Environment (2001)
Potential outliers are measurements that are extremely large or small relative to the rest of the data and therefore are suspected of misrepresenting the population from which they were collected (US EPA 2006b). Outliers may result from:
    * transcription errors
    * data-coding errors
    * measurement problems
    * true extreme values (hotspots).
Graphical displays of data, for example probability plots (concentration plotted against cumulative frequency), and x-y scatter plots (for example, ratios of contaminants expected to be associated with each other), can assist with identifying outliers.  Evaluation of a combination of graphical displays with reference to relevant site layout diagrams is recommended.

It can be tempting to dismiss unexpectedly high values as 'outliers'; however, this is not good practice, as a more thorough examination of the reasons for these unexpected values may lead to new insights into the data (such as the presence of an unsuspected hotspot of contamination) or to reconsideration of underlying assumptions about the data and its distribution.

Potential outliers should be checked for human error due to transcription/data-coding errors and invalid measurements from malfunctioning equipment. The former may be corrected whereas the latter can properly be discarded. Following the procedure outlined in Section 13.1 should minimise the impact of outliers from these causes.

If an outlier is not due to human error, then consider the available qualitative information