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ChristianKl comments on Open thread, 11-17 March 2014 - Less Wrong Discussion
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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.
Don't calibrate on a single variable.
But you do need to assume that somehow you can predict novel events based on previous data
Just going back to my stock market example, what variables would I have calibrated on to predict 9/11 and it's effects on the stock market?
I'm not arguing that you can predict the stock market. What you can do is calibrate yourself enough to see that it's frequently doing things that you didn't predict.