TY  - JOUR
AB  - 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.
AU  - Kelter, Riko
DA  - 2020
DO  - 10.25819/ubsi/8818
KW  - t-Test
KW  - Two-sample t-test
KW  - Effect size
KW  - Treatment effect between two groups
KW  - Markov-Chain-Monte-Carlo
KW  - Bayesian statistics
LA  - eng
PY  - 2020
TI  - bayest: an R-package for effect-size targeted Bayesian two-sample t-tests
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:467-18511
Y2  - 2024-12-26T21:01:11
ER  -