This article introduces JASP, an open-source, user-friendly software for conducting Bayesian hypothesis tests. JASP is designed to bridge the gap between Bayesian theory and practice by providing a comprehensive set of tools for standard statistical analyses, including t-tests, ANOVA, correlation, regression, and contingency tables. The software is based on the Bayesian analyses implemented in the BayesFactor package for R and offers a graphical user interface (GUI) that is familiar to users of SPSS. JASP allows researchers to perform Bayesian inference by simply dragging and dropping variables into analysis panels, with output dynamically updated as input options are selected. The article outlines the general philosophy behind JASP, emphasizing its transparency, inclusivity, and ease of use. It also presents five concrete examples of Bayesian tests implemented in JASP, including a correlation test, a t-test, a one-way ANOVA, a two-way ANOVA, and a repeated measures ANOVA. Each example illustrates the correct interpretation of Bayesian output and highlights the insights and additional possibilities that Bayesian analysis offers. The article concludes with a discussion of future developments for Bayesian analyses with JASP.This article introduces JASP, an open-source, user-friendly software for conducting Bayesian hypothesis tests. JASP is designed to bridge the gap between Bayesian theory and practice by providing a comprehensive set of tools for standard statistical analyses, including t-tests, ANOVA, correlation, regression, and contingency tables. The software is based on the Bayesian analyses implemented in the BayesFactor package for R and offers a graphical user interface (GUI) that is familiar to users of SPSS. JASP allows researchers to perform Bayesian inference by simply dragging and dropping variables into analysis panels, with output dynamically updated as input options are selected. The article outlines the general philosophy behind JASP, emphasizing its transparency, inclusivity, and ease of use. It also presents five concrete examples of Bayesian tests implemented in JASP, including a correlation test, a t-test, a one-way ANOVA, a two-way ANOVA, and a repeated measures ANOVA. Each example illustrates the correct interpretation of Bayesian output and highlights the insights and additional possibilities that Bayesian analysis offers. The article concludes with a discussion of future developments for Bayesian analyses with JASP.