다중 검정을 위한 생물 통계학
Biostatistics for Multiple Testing
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초록
Multiple testings are instances that contain simultaneous tests for more than one hypothesis. When multiple testings are conducted at the same time, it is more likely that the null hypothesisis rejected, even if the null hypothesis is correct. If individual hypothesis decisions are basedon unadjusted p-values, it is usually more likely that some of the true null hypotheses will berejected. In order to solve the multiple testing problems, various studies have attempted to increasethe power by taking into account the family-wise error rate or false discovery rate andstatistics required for testing hypotheses. This article discuss methods that account for the multiplicityissue and introduces various statistical techniques.
Abstract
Multiple testings are instances that contain simultaneous tests for more than one hypothesis. When multiple testings are conducted at the same time, it is more likely that the null hypothesisis rejected, even if the null hypothesis is correct. If individual hypothesis decisions are basedon unadjusted p-values, it is usually more likely that some of the true null hypotheses will berejected. In order to solve the multiple testing problems, various studies have attempted to increasethe power by taking into account the family-wise error rate or false discovery rate andstatistics required for testing hypotheses. This article discuss methods that account for the multiplicityissue and introduces various statistical techniques.
참고문헌 (14)
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