Bonferroni correction
In statistics, the Bonferroni correction (also known as the Bonferroni method) states that if an experimenter is testing n independent hypotheses on a set of data, then the statistical significance level that should be used for each hypothesis separately is 1/n times what it would be if only one hypothesis were tested.
For example, to test two independent hypotheses on the same data at 0.05 significance level, instead of using a p value threshold of 0.05, one would use a stricter threshold of 0.025.
The Bonferroni correction is a safeguard against multiple tests of statistical significance on the same data, where 1 out of every 20 hypothesis-tests will appear to be significant at the α = 0.05 level purely due to chance. It was developed by Carlo Emilio Bonferroni.
A less restrictive criterion is the rough false discovery rate giving (3/4)0.05 = 0.0375 for n = 2 and (21/40)0.05 = 0.02625 for n = 20.
See also
References
- Abdi, H (2007). "Bonferroni and Sidak corrections for multiple comparisons". In N.J. Salkind (ed.). Encyclopedia of Measurement and Statistics (PDF). Thousand Oaks, CA: Sage.
- Manitoba Centre for Health Policy (MCHP) 2003, Bonferroni Correction, <http://www.umanitoba.ca/centres/mchp/concept/dict/Statistics/bonferroni.html>.
- Perneger, Thomas V, What's wrong with Bonferroni adjustments, BMJ 1998;316:1236-1238 ( 18 April ) <http://www.bmj.com/cgi/content/full/316/7139/1236>
- School of Psychology, University of New England, New South Wales, Australia, 2000, <http://www.une.edu.au/WebStat/unit_materials/c5_inferential_statistics/bonferroni.html>
- Weisstein, Eric W. "Bonferroni Correction." From MathWorld--A Wolfram Web Resource. <http://mathworld.wolfram.com/BonferroniCorrection.html>