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Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods.
American Journal of Public Health · 1996 · Vol. 86(5) · pp. 726–728
Mikel Aickin✉(University of Arizona)Howard Gensler
Abstract
Public health researchers are sometimes required to make adjustments for multiple testing in reporting their results, which reduces the apparent significance of effects and thus reduces statistical power. The Bonferroni procedure is the most widely recommended way of doing this, but another procedure, that of Holm, is uniformly better. Researchers may have neglected Holm's procedure because it has been framed in terms of hypothesis test rejection rather than in terms of P values. An adjustment to P values based on Holm's method is presented in order to promote the method's use in public health research.
Statistical Methods and Bayesian InferenceFood Security and Health in Diverse PopulationsBonferroni correctionStatistical hypothesis testingStatistical significanceStatistical powerTest (biology)StatisticsMultiple comparisons problemPublic healthMedicineMathematics
MeSH terms
AnimalsBiometryHumansPublic HealthResearchSkin NeoplasmsMice
Funding
- University of Arizona Cancer Center
Citations
1,315
FWCI
14.16
field-weighted impact
References
9
Percentile
99%
vs. same field & year
Citations per year
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