0. What’s this about?
I’ve written a number of posts on the value of Bayesian likelihood ratios (a heuristic cousin of the “Bayes Factor”) as an “evidentiary weight” statistic generally, and its value in particular as a remedy for the inferential barrenness of p-values and related statistics used to implement “null hypothesis testing.”
In this post, I want to call attention to another virtue of using likelihood ratios: the contribution they can make to protecting against the type 1 error risk associated with underpowered studies. Indeed, I’m going to try to make the case for using LRs for this purpose instead of a method proposed by stats legend & former Freud expert Andrew Gelman (Gelman & Carlin 2014).
As admittedly elegant, and as admittedly valuable they have been in making people aware of click here to see this kind of amazing stuff!a serious problem, G&C’s statistical indexes inject a form of confirmation bias into the practical assessment of the weight to be afforded empirical studies. Using LRs to expose the “type 1” error risk associated with underpowered studies avoids that.
Or at least that’s what I think. I must be crazy, huh?
CONTINUE–IF YOU DARE!
Read Gelmans’s cool reply here!