Conditional calibration and the sage statistician - ARCHIVED

Being a calibrated statistician means using procedures that in long-run practice basically follow the guidelines of Neyman’s approach to frequentist inference, which dominates current statistical thinking. Being a sage (i.e., wise) statistician when confronted with a particular data set means employing some Bayesian and Fiducial modes of thinking to moderate simple Neymanian calibration, even if not doing so formally. This article explicates this marriage of ideas using the concept of conditional calibration, which takes advantage of more recent simulation-based ideas arising in Approximate Bayesian Computation.