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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We’re excited to announce that we will be organizing two summer workshops in 2020. Similar to previous years, there will be one workshop focused on Bayesian cognitive modeling and one dedicated to Bayesian hypothesis testing in JASP. Unlike previous years, we will also feature Stan in our Bayesian cognitive modeling course! Have you always wanted to know the details of…
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The JASP team has organized many workshops to teach a broader audience about (Bayesian) statistics with JASP. There has been a lot of interest in the workshop materials and, of course, we are happy to share them. So far, we only responded to requests individually, but due to the growing interest we decided to put a collection of workshop materials…
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This post is a teaser for van den Bergh, D., van Doorn, J., Marsman, M., Draws, T., van Kesteren, E.-J., Derks, K., Dablander, F., Gronau, Q. F., Kucharsky, S., Komarlu Narendra Gupta, A. R., Sarafoglou, A., Voelkel, J. G., Stefan, A., Ly, A., Hinne, M., Matzke, D., & Wagenmakers, E.-J. (in press). A tutorial on conducting and interpreting a Bayesian…
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This is a continuation of our series on machine learning methods that have been implemented in JASP (version 0.11 onwards). In this blog post we train a machine learning model to find clusters within our data set. The goal of a clustering task is to detect structures in the data. To do so, the algorithm needs to (1)…
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In our series Interview With A Team Member, we aim to introduce the people behind the JASP project. Today we are interviewing Koen Derks. What is your professional background? I completed my research master in 2018 at the University of Amsterdam with a specialization in Psychological Methods. Due to a growing interest in Bayesian methods and two Artificial…
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We are happy to present two new translations of Dr. Mark Goss-Sampson’s manual Statistical Analysis in JASP: A Guide for Students. Julio Meneses supervised the translation of the manual into Spanish and Catalan. The manual explains how to perform many different types of frequentist analyses in JASP 0.9.2. The translated manuals can be found on the JASP Materials page.
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In this blog post, we show how to train a classification model using JASP’s newly released Machine Learning Module. The goal of a classification task is to predict a categorical target variable based on a (possibly large) set of features/predictors. For instance, based on different concentrations of proteins, a medical specialist might want to classify tissue as “benign” or “malignant”.…
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JASP 0.11 has been released and is now available on our download page. This version adds the Machine Learning module with 13 brand new analyses that can be used for supervised and unsupervised learning. With supervised learning, the goal is to predict a target variable by learning from existing labeled data. The goal of unsupervised learning, on the other hand,…
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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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JASP 0.11 will be released soon. This new version contains the Machine Learning module, including 13 brand new analyses!
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