JASP Tutorial 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.

Part 1: Theory of Bayesian inference

• 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

Part 2: Mechanics of using JASP

• 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