The last decade the interest in hypothesis evaluation using the Bayes factor (as an alternative for null-hypothesis significance testing) has been steadily increasing. Straightforward applications have been implemented in the software package JASP https://jasp-stats.org/ and for users that want more the R packages BayesFactor http://bayesfactorpcl.r-forge.r-project.org/ and Bain https://informative-hypotheses.sites.uu.nl/software/bain/ are available (each of these is free).
This power snack will proceed along the following lines (non-technical, an applied perspective will be used):
- What is the Bayes factor and how should it be interpreted.
- Bayesian Type I and II errors versus classical Type I and II errors.
- Bayesian updating as an alternative for power analysis.
- Beyond the classical null and alternative hypotheses.
- Illustration of an application using JASP.