Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction

Multiple hypothesis testing requires adjusting the standard p-value alpha level to avoid spurious results from repeated experiments. Running a test multiple times increases the chance of a Type I error, or a false positive, as illustrated by the search for superheavy elements. The Bonferroni correction offers a conservative approach by controlling the family-wise error rate, ensuring the probability of even one false positive remains low. Alternatively, the Benjamini-Hochberg procedure controls the false discovery rate, a less strict method preferable when minimizing false negatives is more critical. The choice between these statistical corrections depends on the experimental context and tolerance for different types of errors.