ArticlePsychonomic bulletin & review2018
Bayes factor design analysis: Planning for compelling evidence.
Article in Psychonomic bulletin & review, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 244 papers, 4 of them syntheses that pooled it.
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Who cites it
244 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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2 authors.
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Abstract
A sizeable literature exists on the use of frequentist power analysis in the null-hypothesis significance testing (NHST) paradigm to facilitate the design of informative experiments. In contrast, there is almost no literature that discusses the design of experiments when Bayes factors (BFs) are used as a measure of evidence. Here we explore Bayes Factor Design Analysis (BFDA) as a useful tool to design studies for maximum efficiency and informativeness. We elaborate on three possible BF designs, (a) a fixed-n design, (b) an open-ended Sequential Bayes Factor (SBF) design, where researchers can test after each participant and can stop data collection whenever there is strong evidence for either [Formula: see text] or [Formula: see text], and (c) a modified SBF design that defines a maximal sample size where data collection is stopped regardless of the current state of evidence. We demonstrate how the properties of each design (i.e., expected strength of evidence, expected sample size, expected probability of misleading evidence, expected probability of weak evidence) can be evaluated using Monte Carlo simulations and equip researchers with the necessary information to compute their own Bayesian design analyses.
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