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Creating falseness—How to establish statistical evidence of the untrue
Null hypothesis significance testing is the typical statistical approach in search of the truthfulness of hypotheses. This method does not formally consider the prior credence in the hypothesis, which affects the chances of reaching correct conclusions. When scientifically implausible or empirically...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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John Wiley and Sons Inc.
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5656921/ https://www.ncbi.nlm.nih.gov/pubmed/28960726 http://dx.doi.org/10.1111/jep.12823 |
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author | Lytsy, Per |
author_facet | Lytsy, Per |
author_sort | Lytsy, Per |
collection | PubMed |
description | Null hypothesis significance testing is the typical statistical approach in search of the truthfulness of hypotheses. This method does not formally consider the prior credence in the hypothesis, which affects the chances of reaching correct conclusions. When scientifically implausible or empirically weakly supported hypotheses are tested, there is an increased risk that a positive finding in a test in fact is false positive. This article argues that when scientifically weakly supported hypotheses are tested repeatedly—such as when studying the clinical effects of homeopathy—the accumulation of false positive study findings will risk providing false evidence also in systematic reviews and meta‐analyses. False positive findings are detrimental to science and society, as once published, they accumulate persistent untrue evidence, which risks giving rise to nonpurposive research programmes, policy changes, and promotion of ineffective treatments. The problems with false positive findings are discussed, and advice is given on how to minimize the problem. The standard of evidence of a hypothesis should depend not only on the results of statistical analyses but also on its a priori support. Positive findings from studies investigating hypotheses with poor theoretical and empirical foundations should be viewed as tentative until the results are replicated and/or the hypothesis gains more empirical evidence supporting it as likely to be true. |
format | Online Article Text |
id | pubmed-5656921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-56569212017-11-01 Creating falseness—How to establish statistical evidence of the untrue Lytsy, Per J Eval Clin Pract Personal Views Null hypothesis significance testing is the typical statistical approach in search of the truthfulness of hypotheses. This method does not formally consider the prior credence in the hypothesis, which affects the chances of reaching correct conclusions. When scientifically implausible or empirically weakly supported hypotheses are tested, there is an increased risk that a positive finding in a test in fact is false positive. This article argues that when scientifically weakly supported hypotheses are tested repeatedly—such as when studying the clinical effects of homeopathy—the accumulation of false positive study findings will risk providing false evidence also in systematic reviews and meta‐analyses. False positive findings are detrimental to science and society, as once published, they accumulate persistent untrue evidence, which risks giving rise to nonpurposive research programmes, policy changes, and promotion of ineffective treatments. The problems with false positive findings are discussed, and advice is given on how to minimize the problem. The standard of evidence of a hypothesis should depend not only on the results of statistical analyses but also on its a priori support. Positive findings from studies investigating hypotheses with poor theoretical and empirical foundations should be viewed as tentative until the results are replicated and/or the hypothesis gains more empirical evidence supporting it as likely to be true. John Wiley and Sons Inc. 2017-09-27 2017-10 /pmc/articles/PMC5656921/ /pubmed/28960726 http://dx.doi.org/10.1111/jep.12823 Text en © 2017 The Authors Journal of Evaluation in Clinical Practice Published by John Wiley & Sons Ltd This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Personal Views Lytsy, Per Creating falseness—How to establish statistical evidence of the untrue |
title | Creating falseness—How to establish statistical evidence of the untrue |
title_full | Creating falseness—How to establish statistical evidence of the untrue |
title_fullStr | Creating falseness—How to establish statistical evidence of the untrue |
title_full_unstemmed | Creating falseness—How to establish statistical evidence of the untrue |
title_short | Creating falseness—How to establish statistical evidence of the untrue |
title_sort | creating falseness—how to establish statistical evidence of the untrue |
topic | Personal Views |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5656921/ https://www.ncbi.nlm.nih.gov/pubmed/28960726 http://dx.doi.org/10.1111/jep.12823 |
work_keys_str_mv | AT lytsyper creatingfalsenesshowtoestablishstatisticalevidenceoftheuntrue |