A statistical adjustment that reduces false-positive findings when many tests are run at once.
When a study runs many statistical tests simultaneously (e.g., multiple outcomes, subgroups, or timepoints), the chance of finding at least one 'significant' result by chance alone rises well above 5%. Correction methods (e.g., Bonferroni, Holm, or false discovery rate/Benjamini-Hochberg) adjust the significance threshold or p-values to control this inflated error rate. The choice of method matters clinically: overly conservative corrections (e.g., Bonferroni in large comparison sets) can mask real effects, while uncorrected multiple testing can produce spurious 'positive' findings that don't replicate.
This guide was auto-drafted and is pending editorial review.