Please use this identifier to cite or link to this item: http://dx.doi.org/10.25819/ubsi/8818
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Dokument Type: Article
Title: bayest: an R-package for effect-size targeted Bayesian two-sample t-tests
Authors: Kelter, Riko 
Institute: Department Mathematik 
Free keywords: Two-sample t-test, Effect size, Treatment effect between two groups, Markov-Chain-Monte-Carlo, Bayesian statistics
Dewey Decimal Classification: 510 Mathematik
GHBS-Clases: TKM
TKWM
TKF
TKKC
Issue Date: 2020
Publish Date: 2021
Source: Journal of Open Research Software, 8 (1), S.14. - DOI: http://doi.org/10.5334/jors.290
Abstract: 
Typical situations in research include the comparison of two groups regarding a metric variable, in which case usually the two-sample t-test is applied. While common frequentist two-sample t-tests focus on the difference of means of both groups via a p-value, the quantity of interest in applied research most often is the effect size. Existing Bayesian alternatives of the two-sample t-test replace frequentist significance thresholds like the p-value with the Bayes factor, taking the same testing stance. The R package bayest implements a Markov-Chain-Monte-Carlo algorithm to conduct a Bayesian two-sample t-test which estimates the effect size between two groups, while also providing detailed visualization and analysis of all parameters of interest. Because of its focus on the ease of use and interpretability, clinicians and other users can run this t-test within a few lines of code and find out if differences between two groups are scientifically meaningful, instead of significant.
Description: 
Finanziert aus dem DFG-geförderten Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikel
DOI: http://dx.doi.org/10.25819/ubsi/8818
URN: urn:nbn:de:hbz:467-18511
URI: https://dspace.ub.uni-siegen.de/handle/ubsi/1851
Appears in Collections:Publikationen aus der Universität Siegen

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