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A Review of Bayesian Hypothesis Testing and Its Practical Implementations

We discuss hypothesis testing and compare different theories in light of observed or experimental data as fundamental endeavors in the sciences. Issues associated with the p-value approach and null hypothesis significance testing are reviewed, and the Bayesian alternative based on the Bayes factor i...

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Detalles Bibliográficos
Autores principales: Wei, Zhengxiao, Yang, Aijun, Rocha, Leno, Miranda, Michelle F., Nathoo, Farouk S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871131/
https://www.ncbi.nlm.nih.gov/pubmed/35205456
http://dx.doi.org/10.3390/e24020161
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author Wei, Zhengxiao
Yang, Aijun
Rocha, Leno
Miranda, Michelle F.
Nathoo, Farouk S.
author_facet Wei, Zhengxiao
Yang, Aijun
Rocha, Leno
Miranda, Michelle F.
Nathoo, Farouk S.
author_sort Wei, Zhengxiao
collection PubMed
description We discuss hypothesis testing and compare different theories in light of observed or experimental data as fundamental endeavors in the sciences. Issues associated with the p-value approach and null hypothesis significance testing are reviewed, and the Bayesian alternative based on the Bayes factor is introduced, along with a review of computational methods and sensitivity related to prior distributions. We demonstrate how Bayesian testing can be practically implemented in several examples, such as the t-test, two-sample comparisons, linear mixed models, and Poisson mixed models by using existing software. Caveats and potential problems associated with Bayesian testing are also discussed. We aim to inform researchers in the many fields where Bayesian testing is not in common use of a well-developed alternative to null hypothesis significance testing and to demonstrate its standard implementation.
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spelling pubmed-88711312022-02-25 A Review of Bayesian Hypothesis Testing and Its Practical Implementations Wei, Zhengxiao Yang, Aijun Rocha, Leno Miranda, Michelle F. Nathoo, Farouk S. Entropy (Basel) Review We discuss hypothesis testing and compare different theories in light of observed or experimental data as fundamental endeavors in the sciences. Issues associated with the p-value approach and null hypothesis significance testing are reviewed, and the Bayesian alternative based on the Bayes factor is introduced, along with a review of computational methods and sensitivity related to prior distributions. We demonstrate how Bayesian testing can be practically implemented in several examples, such as the t-test, two-sample comparisons, linear mixed models, and Poisson mixed models by using existing software. Caveats and potential problems associated with Bayesian testing are also discussed. We aim to inform researchers in the many fields where Bayesian testing is not in common use of a well-developed alternative to null hypothesis significance testing and to demonstrate its standard implementation. MDPI 2022-01-21 /pmc/articles/PMC8871131/ /pubmed/35205456 http://dx.doi.org/10.3390/e24020161 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Wei, Zhengxiao
Yang, Aijun
Rocha, Leno
Miranda, Michelle F.
Nathoo, Farouk S.
A Review of Bayesian Hypothesis Testing and Its Practical Implementations
title A Review of Bayesian Hypothesis Testing and Its Practical Implementations
title_full A Review of Bayesian Hypothesis Testing and Its Practical Implementations
title_fullStr A Review of Bayesian Hypothesis Testing and Its Practical Implementations
title_full_unstemmed A Review of Bayesian Hypothesis Testing and Its Practical Implementations
title_short A Review of Bayesian Hypothesis Testing and Its Practical Implementations
title_sort review of bayesian hypothesis testing and its practical implementations
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871131/
https://www.ncbi.nlm.nih.gov/pubmed/35205456
http://dx.doi.org/10.3390/e24020161
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