This actually assumes that the Bayesian model is accurate.
Under Black Swan Theory, you can't use past correct predictions to predict future correct predictions.
For example, most of the variance in the stock market is distributed over a few days in history. I could have calibrated on every day leading up to one of those days and felt confident in my ability to predict the stock market... but just one of those days could have wiped out my portfolio.
Calibration actually makes you necessarily overconfident in the Black Swan view of the world.
You don't need to assume that things are normally distributed to be a Bayesian.
People who calibrate themselves usually don't get more confident through the process but less confident.
I could have calibrated on every day leading up to one of those days and felt confident in my ability to predict the stock market... but just one of those days could have wiped out my portfolio.
Don't calibrate on a single variable.
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.