As Bayesian analysis is becoming more popular, adopters of Bayesian statistics have had to consider new issues that they did not before. What is makes “good” prior? How do I interpret a posterior? What Bayes factor is “big enough”? Although the

*theoretical*arguments for the use of Bayesian statistics are very strong, new and unfamiliar ideas can cause uncertainty in new adopters. Compared to the cozy certainty of \(p<.05\), Bayesian statistics requires more care and attention. In theory, this is no problem at all. But as Yogi Berra said, "In theory there is no difference between theory and practice. In practice there is."

In this post, I discuss the the use of verbal labels for magnitudes of Bayes factors. In short, I don't like them, and think they are unnecessary.