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When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment
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Abstract
Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of (cognitive) neuroscience, psychology, and biomedical science in general. We review these shortcomings and suggest that, after sustained negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. If theoretical predictions are weak we should not rely on all or nothing hypothesis tests. Different inferential methods may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Hypothesis-testing studies should be pre-registered and optimally raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out.
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“In cases where complications arise it can be extremely useful to analyse the data with multiple methods to test the robustness of conclusions24,126.”
From A phylogenetic approach to comparative genomics (2025) · cites this paper as [126]
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“The use of statistical methods has been demonstrated to be suboptimal in some fields.”
From How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation (2018) · cites this paper as [19]
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“Major threats that have high prevalence include the misuse and misinterpretation of null-hypothesis testing methods19,20”
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Metadata
- Authors
- Szucs, D., & Ioannidis, J. P. A.
- Affiliations
- Stanford Medicine (US)
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2017-08-03
- Volume
- 11
- Pages
- 390-390
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC5540883
- OpenAlex
- W2951307730
- Cited by
- 298
- References
- 197
- Citations by year
- 2026: 18 · 2025: 28 · 2024: 21 · 2023: 34 · 2022: 44 · 2021: 31 · 2020: 48 · 2019: 39 · 2018: 29 · 2017: 6
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- James S. McDonnell Foundation (220020370)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- null hypothesis significance testing, replication crisis, statistical inference, preregistration, power analysis, effect size, negative results, statistics education, research methodology critique, hypothesis testing
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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