Bayesian methods are increasingly common across the empirical sciences, but getting started can be daunting. This two-day workshop introduces Bayesian inference for researchers who work quantitatively and would like to add Bayesian tools to their repertoire. No prior experience with Bayesian statistics is required. Both days combine conceptual input with hands-on work in JASP and in R. JASP is a free, open-source statistics program with a graphical interface. Day 1 builds the foundations: what a posterior distribution is, where priors come from and how much they matter, and how Bayesian inferences differ from frequentist ones. Participants fit the basic analyses themselves and see the benefits of the Bayesian framework in action. Peer exchange gives space to relate the material to participants' own research. Day 2 moves to more involved topics: Model comparison/averaging using a regression example, and how to check and report results. Participants work through example datasets from different empirical fields. The day closes with an introduction to R-based Bayesian tools for models beyond the graphical interface of JASP.

Learning objectives — Participants will learn:

Familiarity with standard quantitative methods (t-tests, correlation, regression) from applied use. No prior experience with Bayesian statistics required. Part of Day 2 uses R, prior R experience is not strictly needed and all code is provided.

JASP (free and open source) for the hands-on work on both days. On day 2 we will be using R in RStudio. Instructions for installing the required R-packages will be sent to registered participants in advance of the workshop.

Install JASP before the workshop: https://jasp-stats.org/download

Install R and RStudio

Bring a laptop