This tree-day course teaches skills for informative and transparent evaluation of theory-based hypotheses using p-values, information criteria, and Bayes factors. Participants will learn to apply open-science principles and cutting-edge statistical techniques that ensure maximally informative analyses. Contemporary issues - such as publication bias, questionable research practices, the statistical evaluation of (non-null) hypotheses, and methods for evaluating the same research question with multiple (replication) studies - are also addressed. The course will be non-technical in nature and is targeted at PhD students and researchers who want to apply the presented approaches to their own data.
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In this one-day course, participants will be introduced to informative hypotheses, which are alternatives for the traditional null and alternative hypotheses, and to the AIC-type criterion GORIC and the Bayes factor, which are alternatives for the p-value. This course is applied, hands-on, and build around concepts and not formulas.