In Bayesian inference, do people cheat by rigging the prior?

Ulrich Atz writes in with a question:

A newcomer to Bayesian inference may argue that priors seem sooo subjective and can lead to any answer. There are many counter-arguments (e.g., it’s easier to cheat in other ways), but are there any pithy examples where scientists have abused the prior to get to the result they wanted? And if not, can we rely on this absence of evidence as evidence of absence?

I don’t know. It certainly could be possible to rig an analysis using a prior distribution, just as you can rig an analysis using data coding or exclusion rules, or by playing around with what variables are included in a least-squares regression. I don’t recall ever actually seeing this sort of cheatin’ Bayes, but maybe that’s just because Bayesian methods are not so commonly used.

I’d like to believe that in practice it’s harder to cheat using Bayesian methods because Bayesian methods are more transparent. If you cheat (or inadvertently cheat using forking paths) with data exclusion, coding, or subsetting, or setting up coefficients in a least squares regression, or deciding which “marginally significant” results to report, that can slip under the radar. But the prior distribution—that’s something everyone will notice. I could well imagine that the greater scrutiny attached to Bayesian methods makes it harder to cheat, at least in the obvious way by using a loaded prior.