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A Bayesian predictive approach for dealing with pseudoreplication

Pseudoreplication occurs when the number of measured values or data points exceeds the number of genuine replicates, and when the statistical analysis treats all data points as independent and thus fully contributing to the result. By artificially inflating the sample size, pseudoreplication contrib...

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Detalles Bibliográficos
Autores principales: Lazic, Stanley E., Mellor, Jack R., Ashby, Michael C., Munafo, Marcus R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7012913/
https://www.ncbi.nlm.nih.gov/pubmed/32047274
http://dx.doi.org/10.1038/s41598-020-59384-7
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author Lazic, Stanley E.
Mellor, Jack R.
Ashby, Michael C.
Munafo, Marcus R.
author_facet Lazic, Stanley E.
Mellor, Jack R.
Ashby, Michael C.
Munafo, Marcus R.
author_sort Lazic, Stanley E.
collection PubMed
description Pseudoreplication occurs when the number of measured values or data points exceeds the number of genuine replicates, and when the statistical analysis treats all data points as independent and thus fully contributing to the result. By artificially inflating the sample size, pseudoreplication contributes to irreproducibility, and it is a pervasive problem in biological research. In some fields, more than half of published experiments have pseudoreplication – making it one of the biggest threats to inferential validity. Researchers may be reluctant to use appropriate statistical methods if their hypothesis is about the pseudoreplicates and not the genuine replicates; for example, when an intervention is applied to pregnant female rodents (genuine replicates) but the hypothesis is about the effect on the multiple offspring (pseudoreplicates). We propose using a Bayesian predictive approach, which enables researchers to make valid inferences about biological entities of interest, even if they are pseudoreplicates, and show the benefits of this approach using two in vivo data sets.
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spelling pubmed-70129132020-02-21 A Bayesian predictive approach for dealing with pseudoreplication Lazic, Stanley E. Mellor, Jack R. Ashby, Michael C. Munafo, Marcus R. Sci Rep Article Pseudoreplication occurs when the number of measured values or data points exceeds the number of genuine replicates, and when the statistical analysis treats all data points as independent and thus fully contributing to the result. By artificially inflating the sample size, pseudoreplication contributes to irreproducibility, and it is a pervasive problem in biological research. In some fields, more than half of published experiments have pseudoreplication – making it one of the biggest threats to inferential validity. Researchers may be reluctant to use appropriate statistical methods if their hypothesis is about the pseudoreplicates and not the genuine replicates; for example, when an intervention is applied to pregnant female rodents (genuine replicates) but the hypothesis is about the effect on the multiple offspring (pseudoreplicates). We propose using a Bayesian predictive approach, which enables researchers to make valid inferences about biological entities of interest, even if they are pseudoreplicates, and show the benefits of this approach using two in vivo data sets. Nature Publishing Group UK 2020-02-11 /pmc/articles/PMC7012913/ /pubmed/32047274 http://dx.doi.org/10.1038/s41598-020-59384-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lazic, Stanley E.
Mellor, Jack R.
Ashby, Michael C.
Munafo, Marcus R.
A Bayesian predictive approach for dealing with pseudoreplication
title A Bayesian predictive approach for dealing with pseudoreplication
title_full A Bayesian predictive approach for dealing with pseudoreplication
title_fullStr A Bayesian predictive approach for dealing with pseudoreplication
title_full_unstemmed A Bayesian predictive approach for dealing with pseudoreplication
title_short A Bayesian predictive approach for dealing with pseudoreplication
title_sort bayesian predictive approach for dealing with pseudoreplication
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7012913/
https://www.ncbi.nlm.nih.gov/pubmed/32047274
http://dx.doi.org/10.1038/s41598-020-59384-7
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