Oliver Schultheiss writes:
I am a regular reader of your extremely thought-stimulating and educating blog. I am also one of those psychology researchers who were trained in the NHST tradition and who is now struggling hard to retrain himself to properly understand and use the Bayes approach (I am working on my first paper based on JASP and its Bayesian analysis options). And then tonight I came across this recent blog by Uri Simonsohn, “If you think p-values are problematic, wait until you understand Bayes Factors.”
I assume that I am not the only one who is rattled by this (or I am the only one, and this just reveals my lingering deeper ignorance about the Bayes approach) and I was wondering whether you could comment on Uri’s criticism of Bayes Factors on your own blog.
My reply: I don’t like Bayes factors; see here. I think Bayesian inference is very useful, but Bayes factors are based on a model of point hypotheses that typically does not make sense.
To put it another way, I think that null hypothesis significance testing typically does not make sense. When Bayes factors are used for null hypothesis significance testing, I generally think this is a bad idea, and I don’t think it typically makes sense to talk about the probability that a scientific hypothesis is true.
More discussion here: Incorporating Bayes factor into my understanding of scientific information and the replication crisis. The problem is not so much with the Bayes factor as with the idea of null hypothesis significance testing.