Bayest: An R Package for Effect-Size Targeted Bayesian Two-Sample t-Tests. Issue 1 (15th June 2020)

• Record Type: Journal Article
• Authors: Kelter, Riko
• Is Part Of: Journal of open research software. Volume 8:Issue 1(2020)
• Publication Date: 2020-06-15
• Subjects: two-sample t-test -- effect size -- treatment effect between two groups -- Markov-Chain-Monte-Carlo -- Bayesian statistics; Computer software -- Reusability -- Periodicals; Open source software -- Periodicals; 005
• DOI: 10.5334/jors.290
• Languages: English
• ISSNs: 2049-9647

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.