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Quick and Easy Implementation of the Benjamini-Hochberg Procedure for Controlling the False Positive Rate in Multiple Comparisons
Abstract
Williams, Jones, and Tukey (1999) showed that a sequential approach to controlling the false discovery rate in multiple comparisons, due to Benjamini and Hochberg (1995), yields much greater power than the widely used Bonferroni technique that limits the familywise Type I error rate. The Benjamini-Hochberg (B-H) procedure has since been adopted for use in reporting results from the National Assessment of Educational Progress (NAEP), as well as in other research applications. This short note illustrates that the B-H procedure is extremely simple to implement using widely available spreadsheet software. Given its easy implementation, it is feasible to include the B-H procedure in introductory instruction in inferential statistics, augmenting or replacing the Bonferroni technique.
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Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
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Metadata
- Authors
- Thissen, D., Steinberg, L., & Kuang, D.
- Venue
- Journal of Educational and Behavioral Statistics
- Published
- 2002-03
- Volume
- 27
- Issue
- 1
- Pages
- 77-83
- ISSN
- 1076-9986 (ISSN-L)
- 1935-1054
- OpenAlex
- W122167022
- Cited by
- 974
- References
- 1
- FWCI
- 1.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Thissen, D., Steinberg, L., & Kuang, D. (2002). Quick and Easy Implementation of the Benjamini-Hochberg Procedure for Controlling the False Positive Rate in Multiple Comparisons. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986027001077
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
