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An investigation of the false discovery rate and the misinterpretation of p-values

If you use p=0.05 to suggest that you have made a discovery, you will be wrong at least 30% of the time. If, as is often the case, experiments are underpowered, you will be wrong most of the time. This conclusion is demonstrated from several points of view. First, tree diagrams which show the close...

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Detalles Bibliográficos
Autor principal: Colquhoun, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448847/
https://www.ncbi.nlm.nih.gov/pubmed/26064558
http://dx.doi.org/10.1098/rsos.140216
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author Colquhoun, David
author_facet Colquhoun, David
author_sort Colquhoun, David
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description If you use p=0.05 to suggest that you have made a discovery, you will be wrong at least 30% of the time. If, as is often the case, experiments are underpowered, you will be wrong most of the time. This conclusion is demonstrated from several points of view. First, tree diagrams which show the close analogy with the screening test problem. Similar conclusions are drawn by repeated simulations of t-tests. These mimic what is done in real life, which makes the results more persuasive. The simulation method is used also to evaluate the extent to which effect sizes are over-estimated, especially in underpowered experiments. A script is supplied to allow the reader to do simulations themselves, with numbers appropriate for their own work. It is concluded that if you wish to keep your false discovery rate below 5%, you need to use a three-sigma rule, or to insist on p≤0.001. And never use the word ‘significant’.
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spelling pubmed-44488472015-06-10 An investigation of the false discovery rate and the misinterpretation of p-values Colquhoun, David R Soc Open Sci Review Articles If you use p=0.05 to suggest that you have made a discovery, you will be wrong at least 30% of the time. If, as is often the case, experiments are underpowered, you will be wrong most of the time. This conclusion is demonstrated from several points of view. First, tree diagrams which show the close analogy with the screening test problem. Similar conclusions are drawn by repeated simulations of t-tests. These mimic what is done in real life, which makes the results more persuasive. The simulation method is used also to evaluate the extent to which effect sizes are over-estimated, especially in underpowered experiments. A script is supplied to allow the reader to do simulations themselves, with numbers appropriate for their own work. It is concluded that if you wish to keep your false discovery rate below 5%, you need to use a three-sigma rule, or to insist on p≤0.001. And never use the word ‘significant’. The Royal Society Publishing 2014-11-19 /pmc/articles/PMC4448847/ /pubmed/26064558 http://dx.doi.org/10.1098/rsos.140216 Text en © 2014 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Review Articles
Colquhoun, David
An investigation of the false discovery rate and the misinterpretation of p-values
title An investigation of the false discovery rate and the misinterpretation of p-values
title_full An investigation of the false discovery rate and the misinterpretation of p-values
title_fullStr An investigation of the false discovery rate and the misinterpretation of p-values
title_full_unstemmed An investigation of the false discovery rate and the misinterpretation of p-values
title_short An investigation of the false discovery rate and the misinterpretation of p-values
title_sort investigation of the false discovery rate and the misinterpretation of p-values
topic Review Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448847/
https://www.ncbi.nlm.nih.gov/pubmed/26064558
http://dx.doi.org/10.1098/rsos.140216
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