Bonferroni correction
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In statistics, the Bonferroni correction 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, when testing two hypotheses, instead of a p value of 0.05, one would use a stricter p value 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 be significant at the alpha = 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.
[edit] See also
[edit] References
- Abdi, H (2007). "Bonferroni and Sidak corrections for multiple comparisons", in N.J. Salkind (ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks, CA: Sage.
