Biostatistics for Multiple Testing

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 hypothesis is rejected, even if the null hypothesis is correct. If individual hypothesis decisions are based on unadjusted p-values, it is usually more likely that some of the true null hypotheses will be rejected. In order to solve the multiple testing problems, various studies have attempted to increase the power by taking into account the family-wise error rate or false discovery rate and statistics required for testing hypotheses. This article discuss methods that account for the multiplicity issue and introduces various statistical techniques.