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      P in the right place: Revisiting the evidential value of P‐values

      research-article
      1 , 2 ,
      Journal of Evidence-Based Medicine
      John Wiley and Sons Inc.
      evidence, hypothesis, P‐value, replication, statistics

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          Abstract

          P‐values are often calculated when testing hypotheses in quantitative settings, and low P‐values are typically used as evidential measures to support research findings in published medical research. This article reviews old and new arguments questioning the evidential value of P‐values. Critiques of the P‐value include that it is confounded, fickle, and overestimates the evidence against the null. P‐values may turn out falsely low in studies due to random or systematic errors. Even correctly low P‐values do not logically provide support to any hypothesis. Recent studies show low replication rates of significant findings, questioning the dependability of published low P‐values. P‐values are poor indicators in support of scientific propositions. P‐values must be inferred by a thorough understanding of the study's question, design, and conduct. Null hypothesis significance testing will likely remain an important method in quantitative analysis but may be complemented with other statistical techniques that more straightforwardly address the size and precision of an effect or the plausibility that a hypothesis is true.

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          Most cited references20

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          The ASA's Statement onp-Values: Context, Process, and Purpose

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            Scientific method: statistical errors.

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              Toward evidence-based medical statistics. 1: The P value fallacy.

              An important problem exists in the interpretation of modern medical research data: Biological understanding and previous research play little formal role in the interpretation of quantitative results. This phenomenon is manifest in the discussion sections of research articles and ultimately can affect the reliability of conclusions. The standard statistical approach has created this situation by promoting the illusion that conclusions can be produced with certain "error rates," without consideration of information from outside the experiment. This statistical approach, the key components of which are P values and hypothesis tests, is widely perceived as a mathematically coherent approach to inference. There is little appreciation in the medical community that the methodology is an amalgam of incompatible elements, whose utility for scientific inference has been the subject of intense debate among statisticians for almost 70 years. This article introduces some of the key elements of that debate and traces the appeal and adverse impact of this methodology to the P value fallacy, the mistaken idea that a single number can capture both the long-run outcomes of an experiment and the evidential meaning of a single result. This argument is made as a prelude to the suggestion that another measure of evidence should be used--the Bayes factor, which properly separates issues of long-run behavior from evidential strength and allows the integration of background knowledge with statistical findings.
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                Author and article information

                Contributors
                per.lytsy@pubcare.uu.se
                Journal
                J Evid Based Med
                J Evid Based Med
                10.1111/(ISSN)1756-5391
                JEBM
                Journal of Evidence-Based Medicine
                John Wiley and Sons Inc. (Hoboken )
                1756-5391
                05 November 2018
                November 2018
                : 11
                : 4 ( doiID: 10.1111/jebm.2018.11.issue-4 )
                : 288-291
                Affiliations
                [ 1 ] Department of Public Health and Caring Sciences Uppsala University Uppsala Sweden
                [ 2 ] Department of Clinical Neuroscience Karolinska Institute Stockholm Sweden
                Author notes
                [*] [* ] Correspondence Per Lytsy, Department of Public Health and Caring Sciences, Husargatan 3, Box 564, SE‐75122, Uppsala University, Sweden. Email: per.lytsy@ 123456pubcare.uu.se .
                Article
                JEBM12319
                10.1111/jebm.12319
                6587984
                30398018
                f89dd80a-e448-4d69-b7d1-5293dd916381
                © 2018 The Authors Journal of Evidence-Based Medicine published by Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 September 2018
                : 14 October 2018
                Page count
                Figures: 0, Tables: 0, Pages: 4, Words: 3140
                Categories
                Methodology
                Methodology
                Custom metadata
                2.0
                jebm12319
                November 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.4 mode:remove_FC converted:21.06.2019

                Medicine
                evidence,hypothesis,p‐value,replication,statistics
                Medicine
                evidence, hypothesis, p‐value, replication, statistics

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