A less conservative method to adjust for familywise error rate in neuropsychological research: the Holm's sequential Bonferroni procedure.

Background: The traditional Bonferroni method is a commonly used post hoc hypothesis test to adjust for familywise error rate inflation; however, a less well-known derivative of this test, the Holm's sequential procedure, provides an alternative method for familywise error rate correction. This less conservative approach is particularly relevant to studies investigating neuropsychological functioning because of the extent to which neuropsychological datasets tend to include interrelated outcome measures, reducing the relevance of the universal null hypothesis assumption, on which the traditional Bonferroni method relies.

Methods: Mathematical illustrations demonstrating how to compute the two adjustments are provided. The two methods are compared using a simple hypothetical dataset.

Results: By using the divisors (n - j + 1) in lieu of n, Holm's sequential procedure is guaranteed to never reject fewer hypotheses than the traditional Bonferroni adjustment.

Conclusions: The Holm's sequential procedure corrects for Type I error as effectively as the traditional Bonferroni method while retaining more statistical power. Although the Holm's sequential procedure is more complicated to compute than the traditional Bonferroni method, the Holm's sequential procedure may be a more appropriate method for adjusting familywise error rate inflation in many types of neuropsychological research.