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Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars
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Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
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Chugg, B., Ramdas, A., & Grünwald, P. (2026) · Synthese · doi:10.1007/s11229-026-05779-4
Atilano-Robles, E. (2026) · Revista Mexicana de Derecho Electoral · doi:10.22201/iij.24487910e.2026.24.20981
Networking behaviors under the microscope : examining networking actions from different perspectives
Ziani-Franclet, Z. (2020) · theses.fr (ABES) · doi:10.70675/7a4e09eezaa34z455dz8bb3zb1989e8ccca3
Prediction, Risk, and Illusion
Yu, K., & Robinson, M. M. (2026) · Computational Brain & Behavior · doi:10.1007/s42113-026-00285-9
The FDA wants Bayesian trials, but who chooses the prior?
Qian, L. (2026) · Significance · doi:10.1093/jrssig/qmag038
Falsifying Discriminant Validity of Predictive Algorithms
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Lanfear, C. C., & Oliveira, T. R. (2026) · Journal of Developmental and Life-Course Criminology · doi:10.1007/s40865-026-00314-y
P-Hacking Should Not Make Us Abandon the Principle of Total Evidence
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Gerst, M. D., Rossi, C., Brammer, R., Canty, T. P., Dolan, F., Lempert, R. J., & Rovenskaya, E. (2026) · Climatic Change · doi:10.1007/s10584-026-04260-y
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Azhar, F. (2026) · The British Journal for the Philosophy of Science · doi:10.1086/743555
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Teaching Thoughtful Statistical Inference: Stories for the World Beyond p < 0.05
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Metadata
- Authors
- Mayo, D. G.
- Affiliations
- Virginia Tech (US)
- Corresponding author
- Mayo, Deborah G.
- ORCID
- Mayo, Deborah G.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2018-09-14
- OpenAlex
- W3171064310
- Cited by
- 376
- References
- 325
- Citations by year
- 2026: 32 · 2025: 57 · 2024: 35 · 2023: 42 · 2022: 60 · 2021: 72 · 2020: 43 · 2019: 28 · 2018: 5
- FWCI
- 54.96
- Type
- book
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Logic and Philosophy of Science
- Social Sciences › Arts and Humanities › Philosophy › Literary, Cultural, Historical Analysis
- Keywords
- severe testing, statistical inference, replication crisis, degrees of belief, cherry-picking, selective reporting, inductive reasoning, falsification, pseudoscience, science reform
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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