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• W3137735170 abstract "Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions and it's unclear whether the details of the computational implementation (such as bridge sampling) are unbiased for complex analyses. Here, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors; the first-ever use of simulation-based calibration to test the accuracy and bias of Bayes factor estimates using bridge sampling; a study of the stability of Bayes factors against different MCMC draws and sampling variation in the data; and a look at the variability of decisions based on Bayes factors using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis. (PsycInfo Database Record (c) 2022 APA, all rights reserved)." @default.
• W3137735170 created "2021-03-29" @default.
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• W3137735170 date "2022-03-10" @default.
• W3137735170 modified "2023-10-16" @default.
• W3137735170 title "Workflow techniques for the robust use of bayes factors." @default.
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• W3137735170 doi "https://doi.org/10.1037/met0000472" @default.
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