Several methods are available to adjust for multiple comparisons, each with its own advantages and limitations. Some of the most commonly used methods include:
Bonferroni Correction: This is one of the simplest methods. It involves dividing the significance level (e.g., 0.05) by the number of comparisons. While straightforward, it can be overly conservative, increasing the risk of Type II errors. False Discovery Rate (FDR): Unlike the Bonferroni correction, which controls the probability of any false positives, FDR controls the expected proportion of false positives among the rejected hypotheses. This method is less conservative and more powerful in some contexts. Holm-Bonferroni Method: This is a stepwise procedure that is less conservative than the Bonferroni correction. It adjusts the significance levels sequentially, offering a balance between Type I and Type II errors.
