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…
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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…
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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,…
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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…
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Probability distributions lie at the heart of most statistical analyses and thus are crucial for proper understanding and use of statistics. To help researchers, students, and lecturers work easily with various probability distributions, we created the ‘Distribution’ module which is one of the new features of the upcoming version of JASP. As of now, JASP currently covers 12 basic distributions,…
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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…
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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…
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One of the features that we have recently added to JASP is a Bayesian “A/B test”, that is, a test for the equality of two binomial proportions. This test is especially popular in the analysis of clinical trial data, where the proportion of medical successes (or failures) from a treatment group is contrasted against those from a control group. The…
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I am proud to announce the release of our free textbook Learning Statistics with JASP: A Tutorial for Psychology Students and Other Beginners by Danielle J. Navarro, David R. Foxcroft, and Thomas J. Faulkenberry. This textbook, which is freely downloadable from https://learnstatswithjasp.com, continues the series of open-source adaptations of the popular text Learning Statistics with R by Danielle Navarro. Learning…
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Mark Goss-Sampson’s manual “Statistical analysis with JASP: A guide for students” explains how to perform frequentist analyses in JASP. This manual has recently been updated to JASP 0.10.2.
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One of our latest releases, JASP 0.9.1, came with an exciting new feature: the ability to compute new variables. This post demonstrates how to use this new functionality by going over a few examples. When analyzing data, it can come in handy to create an extra column that contains a new variable, obtained from one or several of the existing…
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