Best (but oft-forgotten) practices: the multiple problems of multiplicity-whether and how to correct for many statistical tests.

Testing many null hypotheses in a single study results in an increased probability of detecting a significant finding just by chance (the problem of multiplicity). Debates have raged over many years with regard to whether to correct for multiplicity and, if so, how it should be done. This article first discusses how multiple tests lead to an inflation of the α level, then explores the following different contexts in which multiplicity arises: testing for baseline differences in various types of studies, having >1 outcome variable, conducting statistical tests that produce >1 P value, taking multiple "peeks" at the data, and unplanned, post hoc analyses (i.e., "data dredging," "fishing expeditions," or "P-hacking"). It then discusses some of the methods that have been proposed for correcting for multiplicity, including single-step procedures (e.g., Bonferroni); multistep procedures, such as those of Holm, Hochberg, and Šidák; false discovery rate control; and resampling approaches. Note that these various approaches describe different aspects and are not necessarily mutually exclusive. For example, resampling methods could be used to control the false discovery rate or the family-wise error rate (as defined later in this article). However, the use of one of these approaches presupposes that we should correct for multiplicity, which is not universally accepted, and the article presents the arguments for and against such "correction." The final section brings together these threads and presents suggestions with regard to when it makes sense to apply the corrections and how to do so.