Bayesian inference for psychology. Part II: Example applications with JASP

TLDR

Bayesian hypothesis testing offers an attractive alternative to p‑value testing by enabling evidence quantification and sequential updating, yet it remains underreported in practice. The paper introduces JASP, an open‑source, cross‑platform, user‑friendly graphical software that enables users to perform Bayesian hypothesis tests on standard statistical problems. JASP, built on the BayesFactor package for R, provides a single‑click interface that turns the practical advantages of Bayesian testing into a user‑friendly solution for common analyses such as t‑tests, ANOVAs, correlations, regressions, and contingency tables.

Abstract

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.