[tel]

> be Bayesian on the inside

Strongly disagree, tbh. Picking one side or the other in this debate is silly. Don't "be Frequentist" so as to avoid Bayesian model building techniques since you'll end up stuck all the time and don't "be Bayesian" so as to look down upon simple, workable, un-motivated estimation procedures with good performance.

[eli_gottlieb]

I didn't mean "look down upon ... workable .. procedures with good performance." I meant a more commonsense sort of "private Bayesianism", where you maintain a healthy skepticism of things that have always failed before, and a healthy reliance on things that have always worked before, even when public scientific discourse purports to show you very strong non-Bayesian evidence.

For example, back in my MSc days, I would run a whole lot of metrics on our dataset, and look for patterns. Sometimes I would find a strong, interesting pattern, and go try to tell my advisor about it. He would ask me to double-check my code for bugs, rerun things, and see if the pattern was still there. Often, it wasn't.

My advisor was nobody's Bayesian, a frequentist (and even a user of purely descriptive statistics, oftentimes) through and through.

So to me, "Bayesian on the inside" ends up meaning, "at least Bayesian enough to look for experimental errors." This attitude has helped me a lot in debugging difficult snafus in industry, too.

[tel]

I see what you're saying now and quite like that. Thanks for clarifying!