Multiple Comparison Correction

When multiple hypotheses are tested, the number of apparently significant results found by chance naturally increases. Multiple comparison corrections are used to control this problem. Methods such as Bonferroni offer stricter protection, while FDR-based approaches provide more balanced strategies. This issue is especially important in feature screening, large-scale model experimentation, bioinformatics, and broad exploratory analysis. With many tests, finding “something” becomes easy; the real question is whether that something is actually trustworthy.