Abstract
A methodology is described that incorporates the intra‐ and intertest variabilityand the biological effect of bioassay data in evaluating the toxicity of single and multiple tests for regulatory decision‐making purposes. The single‐ and multiple‐test regulatory decision probabilities were determined from t values (n − 1, one‐tailed) derived from the estimated biological effect and the associated standard error at the critical sample concentration. Single‐test regulatory decision probabilities below the selected minimum regulatory decision probability identify individual tests as noncompliant. A multiple‐test regulatory decision probability is determined by combining the regulatory decision probability of a series of single tests. A multiple‐test regulatory decision probability below the multiple‐test regulatory decision minimum identifies groups of tests in which the magnitude and persistence of the toxicity is sufficient to be considered noncompliant or to require enforcement action. Regulatory decision probabilities derived from the t distribution were compared with results based on standard and bioequivalence hypothesis tests (t tests and no‐observed‐effect concentrations) using single‐ and multiple‐concentration toxicity test data from an actual national pollutant discharge elimination system monitoring program. The analysis demonstrates that the use of regulatory decision probabilities effectively incorporated the precision of the effect estimate into regulatory decisions at a fixed level of effect. Also, probability‐based interpretation of toxicity tests provides incentive to laboratories to produce, and permit holders to use, high‐quality, precise data, particularly when multiple tests are used in regulatory decisions. These results are contrasted with standard and bioequivalence hypothesis tests in which the intratest precision is a determining factor in setting the biological effect used for regulatory decisions.