Two Guidelines for Bootstrap Hypothesis Testing
Abstract
Two guidelines for nonparametric bootstrap hypothesis testing are highlighted. The first recommends that resampling be done in a way that reflects the null hypothesis, even when the true hypothesis is distant from the null. The second guideline argues that bootstrap hypothesis tests should employ methods that are already recognized as having good features in the closely related problem of confidence interval construction. Violation of the first guideline can seriously reduce the power of a test. Sometimes this reduction is spectacular, since it is most serious when the null hypothesis is grossly in error. The second guideline is of some importance when the conclusion of a test is equivocal. It has no direct bearing on power, but improves the level accuracy of a test.
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Bootstrapping does not require distributional assumptions and provides more accurate inferences, particularly when sample size is small.
“Bootstrapping (Efron 1992, Hall and Wilson 1991) does not require distributional assumptions (e.g., normally distributed errors). Bootstraps also provide more accurate inferences, particularly when the sample size is small.”
From Paradoxical effects of self-awareness of being observed: testing the effect of police body-worn cameras on assaults and aggression against officers (2018) · cites this paper as Hall and Wilson 1991
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- Authors
- Hall, P., & Wilson, S. R.
- ORCID
- Hall, Peter
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1991-06
- Volume
- 47
- Issue
- 2
- Pages
- 757
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2038201838
- Cited by
- 471
- References
- 0
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 31 · 2023: 10 · 2022: 9 · 2021: 19 · 2020: 20 · 2019: 22 · 2018: 16 · 2017: 22 · 2016: 17 · 2015: 18 · 2014: 27 · 2013: 13 · 2012: 15
- FWCI
- 8.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics (publishedVersion)
- Funders
- Samsung Advanced Institute of Technology
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- bootstrap hypothesis testing, nonparametric bootstrap, statistical power, confidence interval construction, null hypothesis
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