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Exact replication: Foundation of science or game of chance?
The need for replication of initial results has been rediscovered only recently in many fields of research. In preclinical biomedical research, it is common practice to conduct exact replications with the same sample sizes as those used in the initial experiments. Such replication attempts, however,...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456162/ https://www.ncbi.nlm.nih.gov/pubmed/30964856 http://dx.doi.org/10.1371/journal.pbio.3000188 |
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author | Piper, Sophie K. Grittner, Ulrike Rex, Andre Riedel, Nico Fischer, Felix Nadon, Robert Siegerink, Bob Dirnagl, Ulrich |
author_facet | Piper, Sophie K. Grittner, Ulrike Rex, Andre Riedel, Nico Fischer, Felix Nadon, Robert Siegerink, Bob Dirnagl, Ulrich |
author_sort | Piper, Sophie K. |
collection | PubMed |
description | The need for replication of initial results has been rediscovered only recently in many fields of research. In preclinical biomedical research, it is common practice to conduct exact replications with the same sample sizes as those used in the initial experiments. Such replication attempts, however, have lower probability of replication than is generally appreciated. Indeed, in the common scenario of an effect just reaching statistical significance, the statistical power of the replication experiment assuming the same effect size is approximately 50%—in essence, a coin toss. Accordingly, we use the provocative analogy of “replicating” a neuroprotective drug animal study with a coin flip to highlight the need for larger sample sizes in replication experiments. Additionally, we provide detailed background for the probability of obtaining a significant p value in a replication experiment and discuss the variability of p values as well as pitfalls of simple binary significance testing in both initial preclinical experiments and replication studies with small sample sizes. We conclude that power analysis for determining the sample size for a replication study is obligatory within the currently dominant hypothesis testing framework. Moreover, publications should include effect size point estimates and corresponding measures of precision, e.g., confidence intervals, to allow readers to assess the magnitude and direction of reported effects and to potentially combine the results of initial and replication study later through Bayesian or meta-analytic approaches. |
format | Online Article Text |
id | pubmed-6456162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64561622019-05-03 Exact replication: Foundation of science or game of chance? Piper, Sophie K. Grittner, Ulrike Rex, Andre Riedel, Nico Fischer, Felix Nadon, Robert Siegerink, Bob Dirnagl, Ulrich PLoS Biol Perspective The need for replication of initial results has been rediscovered only recently in many fields of research. In preclinical biomedical research, it is common practice to conduct exact replications with the same sample sizes as those used in the initial experiments. Such replication attempts, however, have lower probability of replication than is generally appreciated. Indeed, in the common scenario of an effect just reaching statistical significance, the statistical power of the replication experiment assuming the same effect size is approximately 50%—in essence, a coin toss. Accordingly, we use the provocative analogy of “replicating” a neuroprotective drug animal study with a coin flip to highlight the need for larger sample sizes in replication experiments. Additionally, we provide detailed background for the probability of obtaining a significant p value in a replication experiment and discuss the variability of p values as well as pitfalls of simple binary significance testing in both initial preclinical experiments and replication studies with small sample sizes. We conclude that power analysis for determining the sample size for a replication study is obligatory within the currently dominant hypothesis testing framework. Moreover, publications should include effect size point estimates and corresponding measures of precision, e.g., confidence intervals, to allow readers to assess the magnitude and direction of reported effects and to potentially combine the results of initial and replication study later through Bayesian or meta-analytic approaches. Public Library of Science 2019-04-09 /pmc/articles/PMC6456162/ /pubmed/30964856 http://dx.doi.org/10.1371/journal.pbio.3000188 Text en © 2019 Piper et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Perspective Piper, Sophie K. Grittner, Ulrike Rex, Andre Riedel, Nico Fischer, Felix Nadon, Robert Siegerink, Bob Dirnagl, Ulrich Exact replication: Foundation of science or game of chance? |
title | Exact replication: Foundation of science or game of chance? |
title_full | Exact replication: Foundation of science or game of chance? |
title_fullStr | Exact replication: Foundation of science or game of chance? |
title_full_unstemmed | Exact replication: Foundation of science or game of chance? |
title_short | Exact replication: Foundation of science or game of chance? |
title_sort | exact replication: foundation of science or game of chance? |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456162/ https://www.ncbi.nlm.nih.gov/pubmed/30964856 http://dx.doi.org/10.1371/journal.pbio.3000188 |
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