Correction for multiple testing: is there a resolution?

Chest. 2011 Jul;140(1):16-18. doi: 10.1378/chest.11-0523.


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.

Publication types

  • Review

MeSH terms

  • Confidence Intervals
  • Data Interpretation, Statistical*
  • Humans
  • Research Design / standards
  • Research Design / statistics & numerical data*
  • Surveys and Questionnaires