Bayesian inference of population prevalence

Ince, R. A.A. , Paton, A. T., Kay, J. W. and Schyns, P. G. (2021) Bayesian inference of population prevalence. eLife, 10, e62461. (doi: 10.7554/eLife.62461) (PMID:34612811) (PMCID:PMC8494477)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Paton, Mr Angus and Schyns, Professor Philippe and Kay, Dr James and Ince, Dr Robin
Creator Roles:
Ince, R. A.A.Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review and editing
Paton, A. T.Data curation, Formal analysis, Validation, Visualization, Writing – review and editing
Kay, J. W.Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing – review and editing
Schyns, P. G.Conceptualization, Funding acquisition, Writing – review and editing
Authors: Ince, R. A.A., Paton, A. T., Kay, J. W., and Schyns, P. G.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:eLife
Publisher:eLife Sciences Publications
ISSN (Online):2050-084X
Copyright Holders:Copyright © 2021 Ince et al.
First Published:First published in eLife 10: e62461
Publisher Policy:Reproduced under a Creative Commons License
Related URLs:

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304240Beyond Pairwise Connectivity: developing an information theoretic hypergraph methodology for multi-modal resting state neuroimaging analysisRobin InceWellcome Trust (WELLCOTR)214120/Z/18/ZNP - Centre for Cognitive Neuroimaging (CCNi)
172413Brain Algorithmics: Reverse Engineering Dynamic Information Processing Networks from MEG time seriesPhilippe SchynsWellcome Trust (WELLCOTR)107802/Z/15/ZNP - Centre for Cognitive Neuroimaging (CCNi)
307582tbcPhilippe SchynsThe Royal Society (ROYSOC)RSWF\R3\183002NP - Centre for Cognitive Neuroimaging (CCNi)
172046Visual Commonsense for Scene UnderstandingPhilippe SchynsEngineering and Physical Sciences Research Council (EPSRC)EP/N019261/1NP - Centre for Cognitive Neuroimaging (CCNi)