When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this article, we discuss the Romano–Wolf multiple-hypothesis correction and document its implementation in Stata. The Romano–Wolf correction (asymptotically) controls the familywise error rate, that is, the probability of rejecting at least one true null hypothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from using rwolf over other multiple-testing procedures that control the familywise error rate.

当同时考虑多重假设检验时，除非明确考虑了检验框架的多重性，否则标准的统计技术将导致对原假设的过度拒绝。在本文中，我们讨论Romano-Wolf多重假设校正并记录其在Stata中的实现。Romano-Wolf校正（渐近地）控制族错误率，即，拒绝被测假设族中至少一个真实零假设的概率。考虑到它通过从原始数据中重新采样来考虑测试统计数据的依存结构，因此此校正功能比早期的多重测试程序（如Bonferroni和Holm校正）要强大得多。我们描述一个命令，rwolf，该工具可进行此更正，并根据各种模型提供一些示例。我们记录并讨论了使用rwolf而不是其他控制家庭错误率的多重测试过程所带来的性能提升。