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Model-averaged Bayesian t tests

M. Maier, F. Bartoš, DS. Quintana, F. Dablander, DV. den Bergh, M. Marsman, A. Ly, EJ. Wagenmakers

. 2025 ; 32 (3) : 1007-1031. [pub] 20241107

Language English Country United States

Document type Journal Article, Review

Grant support
EP-C-17-017 EPA - United States CEP Register
16.Vici.170.083, 451-17-017 Nederlandse Organisatie voor Wetenschappelijk Onderzoek
743086 UNIFY H2020 European Research Council

One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where the two populations are assumed to have the same variance. As an alternative to both methods, we outline a comprehensive t test framework based on Bayesian model averaging. This new t test framework simultaneously takes into account models that assume equal and unequal variances, and models that use t-likelihoods to improve robustness to outliers. The resulting inference is based on a weighted average across the entire model ensemble, with higher weights assigned to models that predicted the observed data well. This new t test framework provides an integrated approach to assumption checks and inference by applying a series of pertinent models to the data simultaneously rather than sequentially. The integrated Bayesian model-averaged t tests achieve robustness without having to commit to a single model following a series of assumption checks. To facilitate practical applications, we provide user-friendly implementations in JASP and via the RoBTT package in R . A tutorial video is available at https://www.youtube.com/watch?v=EcuzGTIcorQ.

References provided by Crossref.org

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$a One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where the two populations are assumed to have the same variance. As an alternative to both methods, we outline a comprehensive t test framework based on Bayesian model averaging. This new t test framework simultaneously takes into account models that assume equal and unequal variances, and models that use t-likelihoods to improve robustness to outliers. The resulting inference is based on a weighted average across the entire model ensemble, with higher weights assigned to models that predicted the observed data well. This new t test framework provides an integrated approach to assumption checks and inference by applying a series of pertinent models to the data simultaneously rather than sequentially. The integrated Bayesian model-averaged t tests achieve robustness without having to commit to a single model following a series of assumption checks. To facilitate practical applications, we provide user-friendly implementations in JASP and via the RoBTT package in R . A tutorial video is available at https://www.youtube.com/watch?v=EcuzGTIcorQ.
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$a Bartoš, František $u Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands $u Institute of Computer Science, Czech Academy of Sciences, Prague, Czechia
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$a Quintana, Daniel S $u Department of Psychology, University of Oslo, Oslo, Norway $u NevSom, Department of Rare Disorders, Oslo University Hospital, Oslo, Norway $u KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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$a Dablander, Fabian $u Institute for Advanced Study, University of Amsterdam, Amsterdam, Netherlands $u Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands
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$a den Bergh, Don van $u Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
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$a Ly, Alexander $u Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands $u Machine Learning Group, CWI Amsterdam, Amsterdam, The Netherlands
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$a Wagenmakers, Eric-Jan $u Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
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