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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,...

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Autores principales: Piper, Sophie K., Grittner, Ulrike, Rex, Andre, Riedel, Nico, Fischer, Felix, Nadon, Robert, Siegerink, Bob, Dirnagl, Ulrich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
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.
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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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