That Optional Stopping Thing
Remember that optional stopping thing perversesheaf brought up?
I was responding to a work email about some model performance issues, and I realized the email overlapped a lot with the optional stopping problem:
Frequentist confidence intervals are, by definition, what the LW crowd would call calibrated. If you estimate hundreds or thousands of parameters over your career with 95% confidence intervals, then you’ll find that you missed your mark 5% of the time. Frequentist statistics could really be thought of as rigorous way to make sure estimates are “calibrated.”
Bayesian credible intervals have no such calibration guarantees. A Bayesian has no reason to believe his estimates are “calibrated” and studies show that generally Bayesian estimates only overlap with the frequentist confidence intervals asymptotically.
The optional stopping problem is a special case of this where the Bayesian credibly interval is particularly poorly calibrated (0 overlap with the true parameter), but in general Bayesians should not expect to be calibrated.
You know how else you can achieve perfect calibration? Guessing. If you guess randomly over the solution space, you’re perfectly calibrated. If you want to make intervals, just choose the entire interval space 95% of the time, and a blank interval 5% of the time.
Wow, my new epistemic theory is even simpler to calculate than frequentism! And produces just as good results!


