Biostatistics Article 1: How to Interpret a Bayes Factor with Prof. Konstantin Slavin

In this episode, Prof. Konstantin Slavin provides a live reading of a paper produced by Quentin F. Gronau, Garston Liang, and Alexander Thorpe that explore Bayes factors as an alternative to p-values for hypothesis testing. They explain how Bayes factors (BF10) measure evidence for an effect versus no effect, with BF10 > 1 indicating evidence for an effect, and BF10 < 1 indicating evidence against it. The discussion highlights the advantages of Bayes factors, such as quantifying evidence for both the null and alternative hypotheses and allowing for continuous evidence monitoring. Using simple analogies, the article provides clear guidance on interpreting Bayes factors and address common misconceptions. Join us to discover how Bayes factors can improve your understanding and interpretation of statistical data. Biostatistics articles on the INS website How to Interpret a Bayes Factor article