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Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods.
Open accessAbstract
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.
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“Multiple comparisons will be used to test a priori hypotheses and to conduct post-hoc testing, with the alpha rate adjusted using the Holm-Bonferroni method [68].”
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Metadata
- Authors
- Aickin, M., & Gensler, H.
- Affiliations
- University of Arizona (US)
- Venue
- American Journal of Public Health
- Publisher
- American Public Health Association
- Published
- 1996-05
- Volume
- 86
- Issue
- 5
- Pages
- 726-728
- ISSN
- 0090-0036 (ISSN-L)
- 1541-0048
- PMC
- PMC1380484
- OpenAlex
- W2020720070
- Cited by
- 1,346
- References
- 9
- Citations by year
- 2026: 87 · 2025: 84 · 2024: 85 · 2023: 69 · 2022: 85 · 2021: 94 · 2020: 76 · 2019: 75 · 2018: 62 · 2017: 71 · 2016: 58 · 2015: 56 · 2014: 56 · 2013: 42 · 2012: 37
- FWCI
- 11.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Public Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Arizona Cancer Center
- National Cancer Institute (CA-25702)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- multiple comparisons, Bonferroni correction, p-value adjustment, statistical significance, statistical power, hypothesis testing
- MeSH terms
- Animals, Biometry, Humans, Public Health, Research, Skin Neoplasms, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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