- #JASP STATISTICAL SOFTWARE AMSTERDAM UPDATE#
- #JASP STATISTICAL SOFTWARE AMSTERDAM SERIES#
- #JASP STATISTICAL SOFTWARE AMSTERDAM FREE#
In addition, JASP output retains its state, meaning that the input options are not lost – clicking on the output brings the input options back up, allowing for convenient review, discussion, and adjustment of earlier analyses. JASP also uses progressive disclosure, which means that initial output is minimalist and expanded only when the user makes specific requests (e.g., by ticking check boxes).
For instance, output is dynamically updated as the user selects input options, and tables are in APA format for convenient copy-pasting in text editors such as LibreOffice and Microsoft Word. Fourth, JASP has a graphical user interface that was designed to optimize the user’s experience. Third, JASP focuses on the statistical methods that researchers and students use most often to retain simplicity, add-on modules are used to implement more sophisticated and specialized statistical procedures. Second, JASP is inferentially inclusive, featuring classical and Bayesian methods for parameter estimation and hypothesis testing.
#JASP STATISTICAL SOFTWARE AMSTERDAM FREE#
First, JASP is free and open-source, reflecting our belief that transparency is an essential element of scientific practice. The JASP philosophy is based on several interrelated design principles. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R.
#JASP STATISTICAL SOFTWARE AMSTERDAM SERIES#
In Part II of this series we introduce JASP ( ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely.
#JASP STATISTICAL SOFTWARE AMSTERDAM UPDATE#
Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Urn:nbn:nl:ui:29-e9c87767-3874-4d7a-b5b3-c63dcec72585ġ1245.Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. The JASP analyses are implemented in R and a series of R packages.
The ease with which these relatively complex Bayesian techniques are available in JASP encourages their broader adoption and furthers a more inclusive statistical reporting practice. Second, JASP provides some of the recent developments in Bayesian hypothesis testing and Bayesian parameter estimation. First, JASP provides several innovations in user interface design specifically, results are provided immediately as the user makes changes to options, output is attractive, minimalist, and designed around the principle of progressive disclosure, and analyses can be peer reviewed without requiring a “syntax”. JASP is open-source and differentiates itself from existing open-source solutions in two ways. This paper introduces JASP, a free graphical software package for basic statistical procedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. Wagenmakers, E.-J.įMG Psychologische Methodenleer (Psychologie, FMG) Psychology Other Research (FMG)īasic statistics, Bayesian inference, Graphical user interface, JASP, Statistical software, Software, Statistics and Probability, Statistics, Probability and Uncertainty JASP: Graphical statistical software for common statistical designs
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