Bayesian data analysis methods have become more mainstream in recent years, in part due to the availability of user friendly statistics packages that implement Bayesian versions of standard analyses: ANOVA, regression, etc. While tools such as R, WinBUGS, JAGS and Stan are indispensible to Bayesian statisticians, simpler tools such as JASP provide a gentler introduction to Bayesian data analysis! This tutorial (and page!) is a work in progress, but the intention is to provide an introduction that is suitable to psychology students without any previous knowledge of Bayesian methods.

**Philosophy of probability.**Different ideas about what the word "probability" means lead to different ideas about how statistics should be done**Introducing Bayes' rule.**An illustration of what Bayes' rule states about probabilities, with applications to dice rolling**Bayesian reasoning.**Using Bayes' rule as guide for reasoning about the world**Bayesian hypothesis testing.**How to build hypothesis tests out of Bayesian reasoning, using the binomial test as an example

**Introducing JASP.**Basics of using the software, with an illustration of what a simple workflow would look like**Bayesian ANOVA.**How to run a (between-subjects) Bayesian ANOVA in JASP**Bayesian t-test.**Running independent Bayesian t-tests in JASP, with a focus on situations where you have a specific planned analysis in mind**Bayesian regression**. An example of Bayesian regression.**Bayesian contingency tables**. The analysis of contingency tables from a Bayesian perspective, with an example using joint multinomial sampling.**Bayesian binomial test**. Revisiting the binomial test from Part 1, checking that our simple example gives the same answer as JASP