Bayesian inference of population prevalence

Within neuroscience, psychology, and neuroimaging, the most frequently used statistical approach is null hypothesis significance testing (NHST) of the population mean. An alternative approach is to perform NHST within individual participants and then infer, from the proportion of participants showin...

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Autores principales:	Robin AA Ince, Angus T Paton, Jim W Kay, Philippe G Schyns
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	statistics generalisation inference prevalence Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/56f9a507c79743bfae8f9f1f19f7305b
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id	oai:doaj.org-article:56f9a507c79743bfae8f9f1f19f7305b
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spelling	oai:doaj.org-article:56f9a507c79743bfae8f9f1f19f7305b2021-11-24T12:20:06ZBayesian inference of population prevalence10.7554/eLife.624612050-084Xe62461https://doaj.org/article/56f9a507c79743bfae8f9f1f19f7305b2021-10-01T00:00:00Zhttps://elifesciences.org/articles/62461https://doaj.org/toc/2050-084XWithin neuroscience, psychology, and neuroimaging, the most frequently used statistical approach is null hypothesis significance testing (NHST) of the population mean. An alternative approach is to perform NHST within individual participants and then infer, from the proportion of participants showing an effect, the prevalence of that effect in the population. We propose a novel Bayesian method to estimate such population prevalence that offers several advantages over population mean NHST. This method provides a population-level inference that is currently missing from study designs with small participant numbers, such as in traditional psychophysics and in precision imaging. Bayesian prevalence delivers a quantitative population estimate with associated uncertainty instead of reducing an experiment to a binary inference. Bayesian prevalence is widely applicable to a broad range of studies in neuroscience, psychology, and neuroimaging. Its emphasis on detecting effects within individual participants can also help address replicability issues in these fields.Robin AA InceAngus T PatonJim W KayPhilippe G SchynseLife Sciences Publications LtdarticlestatisticsgeneralisationinferenceprevalenceMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	statistics generalisation inference prevalence Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	statistics generalisation inference prevalence Medicine R Science Q Biology (General) QH301-705.5 Robin AA Ince Angus T Paton Jim W Kay Philippe G Schyns Bayesian inference of population prevalence
description	Within neuroscience, psychology, and neuroimaging, the most frequently used statistical approach is null hypothesis significance testing (NHST) of the population mean. An alternative approach is to perform NHST within individual participants and then infer, from the proportion of participants showing an effect, the prevalence of that effect in the population. We propose a novel Bayesian method to estimate such population prevalence that offers several advantages over population mean NHST. This method provides a population-level inference that is currently missing from study designs with small participant numbers, such as in traditional psychophysics and in precision imaging. Bayesian prevalence delivers a quantitative population estimate with associated uncertainty instead of reducing an experiment to a binary inference. Bayesian prevalence is widely applicable to a broad range of studies in neuroscience, psychology, and neuroimaging. Its emphasis on detecting effects within individual participants can also help address replicability issues in these fields.
format	article
author	Robin AA Ince Angus T Paton Jim W Kay Philippe G Schyns
author_facet	Robin AA Ince Angus T Paton Jim W Kay Philippe G Schyns
author_sort	Robin AA Ince
title	Bayesian inference of population prevalence
title_short	Bayesian inference of population prevalence
title_full	Bayesian inference of population prevalence
title_fullStr	Bayesian inference of population prevalence
title_full_unstemmed	Bayesian inference of population prevalence
title_sort	bayesian inference of population prevalence
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/56f9a507c79743bfae8f9f1f19f7305b
work_keys_str_mv	AT robinaaince bayesianinferenceofpopulationprevalence AT angustpaton bayesianinferenceofpopulationprevalence AT jimwkay bayesianinferenceofpopulationprevalence AT philippegschyns bayesianinferenceofpopulationprevalence
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Bayesian inference of population prevalence

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