The Bayesian methodology of Sir Harold Jeffreys as a practical alternative to the p value hypothesis test

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Publication year
2020Author(s)
Number of pages
9 p.
Source
Computational Brain & Behavior, 3, 2, (2020), pp. 153-161ISSN
Publication type
Article / Letter to editor

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Organization
SW OZ DCC AI
Journal title
Computational Brain & Behavior
Volume
vol. 3
Issue
iss. 2
Languages used
English (eng)
Page start
p. 153
Page end
p. 161
Subject
Cognitive artificial intelligenceAbstract
Despite an ongoing stream of lamentations, many empirical disciplines still treat the p value as the sole arbiter to separate the scientific wheat from the chaff. The continued reign of the p value is arguably due in part to a perceived lack of workable alternatives. In order to be workable, any alternative methodology must be (1) relevant: it has to address the practitioners' research question, which - for better or for worse- most often concerns the test of a hypothesis, and less often concerns the estimation of a parameter; (2) available: it must have a concrete implementation for practitioners' statistical workhorses such as the t test, regression, and ANOVA; and (3) easy to use: methods that demand practitioners switch to the theoreticians' programming tools will face an uphill struggle for adoption. The above desiderata are fulfilled by Harold Jeffreys's Bayes factor methodology as implemented in the open-source software JASP. We explain Jeffreys's methodology and showcase its practical relevance with two examples.
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- Academic publications [204107]
- Electronic publications [102394]
- Faculty of Social Sciences [27319]
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