Bayesian statistical inference for health data analysis using JASP 

Presenter: Zita Oravecz, Associate Professor of Human Development and Family Studies
Bayesian methods offer a flexible and powerful framework for addressing questions in health research. In this workshop, participants will learn the fundamental concepts of Bayesian inference, including Bayes' theorem, prior and posterior distributions. I will provide hands-on experience on how to perform Bayesian hypothesis testing, which is especially useful in health research as it allows for quantifying evidence in favor of a null effect (or an effect) and for continuously monitoring accumulation of evidence for competing hypotheses. I will also introduce participants to the use of JASP, an open-source and user-friendly software, for conducting Bayesian statistical inference. Participants are encouraged to download the software before the workshop from jasp-stats.org/.