Bonferroni correction

From Wikipedia, the free encyclopedia

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 is n times smaller than usual. 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 1/20 hypotheses tested will appear be significant at the alpha=0.05 level purely due to chance. It was developed by Carlo Emilio Bonferroni.

[edit] See also

• Bonferroni inequalities

[edit] References

• Abdi, H (2007). “Bonferroni and Sidak corrections for multiple comparisons”, N.J. Salkind (ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks, CA: Sage.