| Abstrakt: |
We propose a framework on sample size for species sensitivity distribution (SSD) analyses, with perspectives on Bayesian, frequentist, and even nonparametric approaches to estimation. The intent of a statistical sample size analysis is to ensure that the implementation of a statistical model will satisfy a minimum performance standard when relevant conditions are met. It requires that a statistical model be fully specified and that the means of measuring its performance as a function of sample size be detailed. Defining the model conditions under which sample size is calculated is often the most difficult, and important, aspect of sample size analysis because if the model is not representative, then the sample size analysis will provide incorrect guidance. Definitive guidance on sample size requires general agreement on representative models and their performance from stakeholders in important domains such as chemical safety assessments involving government regulators and industry; the present study provides an initial framework that could be used to this end in the future. In addition, our analysis provides immediate value for understanding how well current SSD analyses perform under a few basic models, sample sizes, and quantitative performance criteria. The results confirm that many analyses are adequately sized to estimate hazardous concentration percentile values (typically the 5th percentile for chemical hazard assessments). However, on the low end of sizes seen in common practice, hazardous concentration estimates can be more than 1 order of magnitude greater than the model‐defined value. Environ Toxicol Chem 2019;38:1514–1525. © 2019 SETAC [ABSTRACT FROM AUTHOR] |
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