This blogpost provides an overview of the JASP tutorials available on YouTube. We will discuss the available tutorials and highlight some of the videos. First, the video’s by Erin Buchanan, professor in Cognitive Analytics at Harrisburg University of Science and Technology. Erin Buchanan owns the YouTube page ‘Statistics of DOOM’. The channel contains video tutorials for different statistical programs as…
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We are delighted to announce that registration is now open for the annual Amsterdam workshop on probabilistic modelling for cognitive science. The tenth installment of this workshop takes place August 17-21, 2020. Program In this workshop, plenary lectures provide the theoretical background of Bayesian statistics, and practical computer exercises teach participants how to use the popular JAGS and Stan programs…
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The JASP Team is excited to announce that registration for our annual two-day Amsterdam workshop is now open. The workshop takes place on August 24-25, 2020. Program In this two-day workshop, plenary lectures provide the theoretical background of Bayesian statistics and Bayesian hypothesis testing; in addition, practical exercises demonstrate how the JASP program is applied to a wide range of…
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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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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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