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An Interactive App for Designing Informative Experiments

Bayesian inference offers the pragmatic researcher a series of perks (Wagenmakers, Morey, & Lee, 2016). For instance, Bayesian hypothesis tests can quantify support in favor of a null hypothesis, and they allow researchers to track evidence as data accumulate (e.g., Rouder, 2014).

However, Bayesian inference also confronts researchers with new challenges, for instance concerning the planning of experiments. Within the Bayesian paradigm, is there a procedure that resembles a frequentist power analysis? (yes, there is!)


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