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Plots of P-values to evaluate many tests simultaneously
Abstract
When a large number of tests are made, possibly on the same data, it is proposed to base a simultaneous evaluation of all the tests on a plot of cumulative P-values using the observed significance probabilities. The points corresponding to true null hypotheses should form a straight line, while those for false null hypotheses should deviate from this line. The line may be used to estimate the number of true hypotheses. The properties of the method are studied in some detail for the problems of comparing all pairs of means in a one-way layout, testing all correlation coefficients in a large correlation matrix, and the evaluation of all 2×2 subtables in a contingency table. The plot is illustrated on real data.
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Citations
Cites
Use of Half-Normal Plots in Interpreting Factorial Two-Level Experiments
Daniel, C. (1959) · Technometrics · doi:10.1080/00401706.1959.10489866 · Cited by 798
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More powerful procedures for multiple significance testing
Hochberg, Y., & Benjamini, Y. (1990) · Statistics in Medicine · doi:10.1002/sim.4780090710 · Cited by 2,715
… and the Cross-Section of Expected Returns
Harvey, C. R., Liu, Y., & Zhu, H. (2016) · Review of Financial Studies · doi:10.1093/rfs/hhv059 · Cited by 2,119
On the adaptive control of the false discovery rate in multiple testing with independent statistics
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Metadata
- Authors
- SCHWEDER, T., & SPJØTVOLL, E.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1982
- Volume
- 69
- Issue
- 3
- Pages
- 493-502
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2057758736
- Cited by
- 434
- References
- 1
- FWCI
- 8.53
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
