A Model of Multiple Hypothesis Testing
为多重假设检验实践提供了经济学基础，通过考虑研究成本与假设数量的关系，推导出何时需要进行多重比较校正，并利用药物审批和项目评估的实际成本数据计算了临界值。
Abstract Multiple hypothesis testing (MHT) practices vary widely, without consensus on which are appropriate when. This article provides an economic foundation for these practices designed to capture leading examples, such as regulatory approval on the basis of clinical trials. MHT adjustments are appropriate in our framework to the extent that research costs are invariant to the number of hypotheses. Control of average size, as for example via a Bonferroni correction, emerges in the limit case where all costs are fixed; in the opposite limit, where costs vary in proportion to the hypothesis count, no correction is needed. We illustrate implications by calculating explicit critical values using data on actual costs in the drug approval process and in program evaluation research; these suggest that some MHT adjustment is warranted in these applications, but not as much as implied by standard practice.