Bonferroni Correction

The Bonferroni correction is a statistical adjustment that controls the family-wise error rate when multiple hypothesis tests are performed on the same data. It divides the target significance threshold (commonly 0.05) by the number of comparisons, so with three pairwise tests each must reach p < 0.0167 to be considered significant. Accessibility and HCI studies routinely apply Bonferroni correction when running post-hoc pairwise tests after omnibus tests such as Friedman or ANOVA - it is conservative but simple, and avoids the inflated false-positive rate that naively running multiple tests would produce.