Specify Acceptance Criteria Sample Clauses

Specify Acceptance Criteria. In evaluating the results of any study, two types of decision errors are possible: ▪ A false negative decision error occurs when it is decided that ingestion of game tissues is safe when it is not safe. ▪ A false positive decision error occurs when it is decided that ingestion of game tissues is not safe when it is safe. The EPA is most concerned about guarding against the occurrence of false negative decision errors, since an error of this type may leave humans exposed to unacceptable levels of LA in game tissue. To minimize the chances of underestimating the true amount of exposure and risk, the EPA generally recommends that risk calculations be based on the 95% upper confidence limit (95UCL) of the sample mean (EPA 1992). However, applications currently utilized to calculate 95UCLs (i.e., ProUCL) are not designed for asbestos data sets and are not recommended for use (EPA 2008). The EPA is presently working to develop a new software application that will be appropriate for use with asbestos data sets, but the application is not yet available for use. Because the 95UCL cannot presently be calculated with confidence, risk calculations will be based on the sample mean, only, as recommended by EPA (2008). This means that risk estimates may be either higher or lower than true values, and this will be identified as a source of uncertainty in the risk assessment. The EPA is also concerned with the probability of making false positive decision errors. Although this type of decision error does not result in unacceptable human exposure, it may result in unnecessary expenditure of resources. Because it is not possible at present to quantify the uncertainty in the mean of an asbestos data set as a function of the number of samples, it is not possible to specify a minimum number of samples required to minimize the risk of false positive decision errors. In addition, the number of game animals that can be sacrificed is limited, thus it may not be possible to collect enough samples to minimize the potential for making false positive decision errors.
Sample 1
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