If you’re interested in the Box-Cox power transformation . . .

You can read this post from Danielle Navarro.

I also recommend Section 7.6 of Bayesian Data Analysis, which extends an example of Rubin where the Box-Cox power transformation fails. We do some cool stuff there, including a posterior predictive check that reveals the problem, and an extension of the model by incorporating a bound on the extreme end of the distribution.

The entire BDA book can be downloaded at the above link, but for convenience I’m including Section 7.6 right here.