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A standard practice in statistical hypothesis testing is to mention the p-value alongside the accept/reject decision.
P. Grünwald, Rianne de Heide, and Wouter Koolen · 1906
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On the problem of the most efficient tests of statistical hypotheses
J. Neyman and E. S Pearson · 1933
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Contributions to the theory of statistical estimation and testing hypotheses
Abraham Wald · 1939
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J. Neyman · 1950
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Some problems connected with statistical inference
David R Cox · 1958
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E.L. Lehmann · 1959
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D.R. Cox and D.V. Hinkley · 1974
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J. Neyman · 1976
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Likelihood
A.W.F. Edwards · 1984
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Statistical Decision Theory and Bayesian Analysis
J.O. Berger · 1985
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Testing a point null hypothesis: The irreconcilability of p values and evidence (with discussion and rejoinder)
J. Berger and T. Sellke · 1987
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The Fisher, Neyman-Pearson theories of testing hypotheses: One theory or two?
E.L. Lehmann · 1993
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Probability and Measure
Patrick Billingsley · 1995
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Statistical evidence: a likelihood paradigm
Richard Royall · 1997
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Could Fisher, Jeffreys and Neyman have agreed on testing?
J.O. Berger · 2003
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Confusion over measures of evidence (p’s) versus errors ( α \alpha ’s) in classical statistical testing
R. Hubbard and M.J. Bayarri · 2003
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Alphabet soup: Blurring the distinctions between p p ’s and α \alpha ’s in psychological research
R. Hubbard · 2004
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Frequentist statistics as a theory of inductive inference
Deborah G Mayo and David R Cox · 2006
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Test martingales, Bayes factors and p-values
Glenn Shafer, Alexander Shen, Nikolai Vereshchagin, and Vladimir Vovk · 2011
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Uniformly most powerful Bayesian tests
Valen E Johnson · 2013
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Inferential models: reasoning with uncertainty
Ryan Martin and Chuanhai Liu · 2015
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Generalized fiducial inference: A review and new results
J. Hannig, H. Iyer, R.C.S. Lai, and T.C.M. Lee · 2016
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Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions
T. Schweder and N. Hjort · 2016
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In gentle praise of significance tests, 2018
David R. Cox · 2018
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Safe probability
Peter Grunwald · 2018
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Statistical inference as severe testing: How to get beyond the statistics wars
Deborah G Mayo · 2018
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Divergence vs. decision p-values: A distinction worth making in theory and keeping in practice
Sander Greenland · 2022
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E-values as unnormalized weights in multiple testing
Nikolaos Ignatiadis, Ruodu Wang, and Aaditya Ramdas · 2022
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Anytime-valid linear models and regression adjusted causal inference in randomized experiments
Michael Lindon, Dae Woong Ham, Martin Tingley, and Iavor Bojinov · 2022
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E-statistics, group invariance and anytime valid testing
Muriel Felipe Pérez-Ortiz, Tyron Lardy, Rianne de Heide, and Peter Grünwald · 2022
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ALL-IN meta-analysis: breathing life into living systematic reviews
J. ter Schure and P. Grünwald · 2022
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Admissible anytime-valid sequential inference must rely on nonnegative martingales
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The support interval
Eric-Jan Wagenmakers, Quentin F Gronau, Fabian Dablander, and Alexander Etz · 2020
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Universal inference
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R-package safestats
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game-theoretic statistics and safe anytime-valid inference
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