title: Correction for multiple testing: is there a resolution?
abstract: In most studies, many statistical tests are performed. They can be run to compare the groups at baseline, look at relationships among the various measures, and, for intervention trials, examine more than one end point. As the number of tests increases, so does the probability of finding at least one of them to be statistically significant just by chance (the problem of multiplicity). A number of procedures have been developed to deal with multiplicity, such as the Bonferroni correction, but there is continuing controversy regarding if and when these procedures should be used. In this article, we offer recommendations about when they should and should not be brought into play.
authors: Department of Psychiatry and Behavioural Neurosciences, Hamilton, ON, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada. Electronic address: streiner@mcmaster.ca.
authors: David L
authors: DL
authors: Streiner
authors: Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada.
authors: Geoffrey R
authors: GR
authors: Norman
mesh_terms: D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D012107:Research Design / Q000592:standards / Q000706:statistics & numerical data*; D011795:Surveys and Questionnaires