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This three-day course discusses the evaluation of theory-based hypotheses using p-values, the Bayes factor, and information criteria. Contemporary phenomena will be covered, like publication bias; questionable research practices; the replication crisis; the statistical evaluation of replication studies; and studies in which multiple data sets are used to evaluate the same research question. The course will be non-technical in nature, that is, it is targeted at students and researchers who want to use the approaches presented for the evaluation of their own data.

This course introduces all the essential ingredients needed to start Bayesian estimation and inference. We discuss specifying priors, obtaining the posterior, prior/posterior predictive checking, sensitivity analyses, and the usefulness of a specific class of priors called shrinkage priors. We propose strategies for reproducibility and reporting standards, outlining the WAMBS-checklist (when to Worry and how to Avoid the Misuse of Bayesian Statistics). We have prepared many exercises to enable students to get hands-on experience.