The JASP Blog

When students are first confronted with Bayesian statistics they have to become familiar with key concepts that differ fundamentally from those that they were taught in frequentist courses. To assist the transition to Bayesian inference we recently created the “Learn Bayes” module in JASP (with support from a grant from the APS Fund for Teaching and Public Understanding of Psychological…

From JASP 0.13 onwards, it is possible to save JASP graphs “as pptx”, courtesy of the R package “officer”. The resulting .pptx file can then be easily edited in Powerpoint or its open-source cousin Impress. Hence, “save as pptx” offers a new opportunity to edit JASP graphs. Obviously this is a temporary patch and not the ultimate solution; full graph…

The goal of this JASP blog post is threefold: To demonstrate the improved ability to annotate analyses. For annotations, JASP 0.12 now uses Quill. As stated on https://quilljs.com/, “Quill is a free, open source WYSIWYG editor built for the modern web. With its modular architecture and expressive API, it is completely customizable to fit any need.“ To demonstrate the ease…

A recent addition to JASP is the Bayesian A/B test. This test compares two proportions and quantifies the extent to which the data undercut or support the hypothesis that a treatment has an effect. As the test requires only four numbers (i.e., the number of successes and failures in each of two groups), many clinical trials lend themselves to an…

Data from the reproducibility project. The x-axis shows the effect size of the original studies, the y-axis shows the effect size of the replications. The color indicates whether a replication was significant (purple) or not (black). A linear regression line is fit for each group. Figure from JASP. If there is one thing that caused widespread adoption of Bayesian inference,…

This is a guest post by Dustin Fife, responsible for the Visual Modeling module in JASP. Years ago when I worked as a biostatistician, I was assigned to analyze the data for a local luminary in the field of Muscular Sclerosis. This analysis would lead to a conference submission, at least, and likely a publication. The man provided me a…

Probability distributions lie at the heart of most statistical analyses and thus are crucial for proper understanding and use of statistics. To help users work easily with various probability distributions, we created the ‘Distribution’ module. As of now, JASP currently covers 12 basic distributions, each available as a stand alone analysis panel: Continuous Normal Student’s t F-distribution Chi-squared Beta Gamma…

This post was inspired by a conversation I had with Henrik Singmann, maintainer of the glorious afex package. The latest iteration of JASP, version 0.12, features a much sought after functionality in ANOVA’s: specifying custom contrasts! This development sparked a lively discussion with some team members about the available options when following up on a detected main effect in an…

Over the past few years, we’ve found that mediation and moderation analysis are highly requested features. Since version 0.10.1, JASP can do both! This blogpost goes through two introductory examples, showing how mediation and moderation can be performed in JASP. Mediation means that the effect of a variable X on variable Y is (partially) indirect, through the variable M. Moderation…