Jump to: Page Content, Site Navigation, Site Search,
You are seeing this message because your web browser does not support basic web standards. Find out more about why this message is appearing and what you can do to make your experience on this site better.
Thomas V Perneger Institute of Social and
Preventive Medicine, University of Geneva, CH-1211 Geneva 4, Switzerland
Correspondence to: Dr Perneger perneger@cmu.unige.ch
The first 150 words of the full text of this article appear below.
When more than one statistical test is performed in analysing the data from a clinical study, some statisticians and journal editors demand that a more stringent criterion be used for "statistical significance" than the conventional P<0.05.1 Many well meaning researchers, eager for methodological rigour, comply without fully grasping what is at stake. Recently, adjustments for multiple tests (or Bonferroni adjustments) have found their way into introductory texts on medical statistics, which has increased their apparent legitimacy. This paper advances the view, widely held by epidemiologists, that Bonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inference.
| Table Removed (Available Only in the Full Text) |
| |
Adjustment for multiple tests |
|---|
Bonferroni adjustments are based on the following
reasoning.1-3 If a null hypothesis is true (for instance,
two treatment groups in a randomised trial do not differ in terms
of cure rates), a significant difference (P<0.05) will be observed by
chance once in 20 trials. This is the type I error, or 
Read all Rapid Responses