A statistical correction method used in multiple comparisons that adjusts significance levels to control the overall probability of making a Type I error. It divides the desired alpha level by the number of comparisons being made.
Named after Italian mathematician Carlo Emilio Bonferroni (1892-1960), who developed inequality principles that form the basis of this correction. The statistical application was formalized in the mid-20th century for multiple testing problems.
The Bonferroni correction is like a strict bouncer at the significance party - it makes the entrance requirements much tougher when you're making multiple comparisons, because the more tests you run, the more likely you are to find false positives by pure chance!
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