A Tutorial on Bayes Factor Design Analysis with Informed Priors
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Description: Well-designed experiments are likely to yield compelling evidence with efficient sample sizes. Bayes Factor Design Analysis (BFDA) is a recently developed methodology that allows researchers to balance the informativeness and eﬃciency of their experiment (Schönbrodt & Wagenmakers, 2017). With BFDA, researchers can control the rate of misleading evidence but, in addition, they can plan for a target strength of evidence. BFDA can be applied to ﬁxed-N and sequential designs. In this tutorial paper, we provide a tutorial-style introduction to BFDA and generalize the method to informed prior distributions. We also present a user-friendly web-based BFDA application that allows researchers to conduct BFDAs with ease. Two practical examples highlight how researchers can use a BFDA to plan for informative and eﬃcient research designs.