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The established language for statistical testing --- significance levels, power, and p-values --- is overly complicated and deceptively conclusive.
Accumulation bias in meta-analysis: The need to consider time in error control, 2019
Judith ter Schure and Peter Grünwald · 1905
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Peter D. Grünwald, Rianne de Heide, and Wouter M. Koolen · 1906
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On the mathematical foundations of theoretical statistics
Ronald A. Fisher · 1922
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On the use and interpretation of certain test criteria
Jerzy Neyman and Egon S. Pearson · 1928
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Wahrscheinlichkeit, Statistik und Wahrheit
Richard von Mises · 1928
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Outline of a theory of statistical estimation based on the classical theory of probability
Jerzey Neyman · 1937
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Some difficulties of interpretation encountered in the application of the chi-square test
Joseph Berkson · 1938
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Étude critique de la notion de collectif
Jean Ville · 1939
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A new interpretation of information rate
John L. Kelly Jr · 1956
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Some problems connected with statistical inference
David R. Cox · 1958
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Statistical Methods for Research Workers
Ronald A. Fisher · 1958
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Information Theory and Statistics
Solomon Kullback · 1959
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Optimal gambling systems for favorable games
Leo Breiman · 1961
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A Bayesian test of some classical hypotheses — with applications to sequential clinical trials
Jerome Cornfield · 1966
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Zufälligkeit und Wahrscheinlichkeit. Eine algorithmische Begründung der Wahrscheinlichkeitstheorie
Claus-Peter Schnorr · 1971
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Likelihood. An account of the statistical concept of likelihood and its application to scientific inference
A. W. F. Edwards · 1972
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Statistical Methods and Scientific Inference
Ronald A. Fisher · 1973
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Proceedings of Conference on Foundational Questions in Statistical Inference, Aarhus, May 7-12, 1973
Ole Barndorff-Nielsen, Preben Blæsild, and Geert Schou, editors · 1974
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Teoria Delle Probabilità
Bruno de Finetti · 1975
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Uniform tests of randomness (in Russian)
Leonid A. Levin · 1976
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The History of Statistics: The Measurement of Uncertainty before 1900
Stephen M. Stigler · 1986
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Statistical inference: Selected correspondence of R. A. Fisher
J. H. Bennett, editor · 1990
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Statistical Reasoning with Imprecise Probabilities
Peter Walley · 1991
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The direct use of likelihood for significance testing
A. P. Dempster · 1997
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Statistical Evidence: A Likelihood Paradigm
Richard M. Royall · 1997
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Fisher and regression
A systematic statistical approach to evaluating evidence from observational studies
David Madigan et al · 2014
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Estimating the reproducibility of psychological science
Open Science Collaboration · 2015
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Rejection odds and rejection ratios: A proposal for statistical practice in testing hypotheses
M. J. Bayarri, Daniel J. Benjamin, James O. Berger, and Thomas M. Sellke · 2016
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The ASA’s statement on p-values: Context, process, and purpose
Ronald L. Wasserstein and Nicole A. Lazar · 2016
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Some natural solutions to the p-value communication problem—and why they won’t work
Andrew Gelman and John Carlin · 2017
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The scientific outlook in financial economics
Campbell R. Harvey · 2017
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John Aldrich · 2005
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Prediction, Learning, and Games
Nicolò Cesa-Bianchi and Gábor Lugosi · 2006
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Elements of Information Theory
Thomas M. Cover and Joy A. Thomas · 2006
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Severe testing as a basic concept in a Neyman-Pearson philosophy of induction
Deborah G. Mayo and Aris Spanos · 2006
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History of applications of martingales in survival analysis
Odd O. Aalen, Per Kragh Andersen, Ørnulf Borgan, Richard D. Gill, and Niels Keiding · 2009
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On the history of martingales in the study of randomness
Laurent Bienvenu, Glenn Shafer, and Alexander Shen · 2009
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Statistical significance and the dichotomization of evidence
Blakeley B. McShane and David Gal · 2017
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When should modes of inference disagree? Some simple but challenging examples
D. A. S. Fraser, Nancy Reid, and Wei Lin · 2018
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Statistical rituals: The replication delusion and how we got there
Gerd Gigerenzer · 2018
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Beyond ‘significance’: principles and practice of the analysis of credibility
Robert A. Matthews · 2018
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Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars
Deborah Mayo · 2018
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Improving reproducibility by using high-throughput observational studies with empirical calibration
Martijn J. Schuemie et al · 2018
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Retire statistical significance
Valentin Amrhein, Sander Greenland, and Blake McShane et al · 2019
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The false positive risk: A proposal concerning what to do about p-values
David Colquhoun · 2019
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Is there a confidence interval for that? A critical examination of null outcome reporting in accounting research, 2019
William M. Cready, Jiapeng He, Wenwei Lin, Chengdao Shao, Di Wang, and Yang Zhang · 2019
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On the nineteenth century origins of significance testing and p-hacking, 2019
Glenn Shafer · 2019
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Game-Theoretic Foundations for Probability and Finance
Glenn Shafer and Vladimir Vovk · 2019
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Moving to a world beyond “ p < 0.05 p<0.05 ”
Ronald L. Wasserstein, Allen L. Schirm, and Nicole A. Lazar · 2019
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