ONE OF THE FUNDAMENTAL CONCEPTS OF BAYESIAN STATISTICS IS THAT YOU ARE ABLE TO UPDATE YOUR INITIAL HYPOTHESIS ABOUT HOW A POPULATION OF INTEREST IS DISTRIBUTED BASED ON NEW INFORMATION. THUS, YOU DON’T CONFIRM YOUR PRIORS, YOU UPDATE THEM IN RESPONSE TO INFORMATION DERIVED FROM A SAMPLE (WHICH CONTINUES TO BE A SYSTEMATIC SET OF MEASUREMENTS OF MORE THAN ONE THING AND HAS NOT, NOR HAS IT EVER BEEN, WHAT A JOURNALIST SEES ON A TV SHOW), AND THE EXTENT TO WHICH YOU UPDATE THEM REVEALS INFORMATION ABOUT THE QUALITY OF THOSE INITIAL ASSUMPTIONS. TO STATE THAT YOU ARE CONFIRMING YOUR PRIORS IS TO REVEAL THAT YOU NEVER INTENDED TO REVISIT YOUR ASSUMPTIONS IN THE FIRST PLACE AND ARE INCAPABLE OF REASONING, NOT ENGAGING IN IT.
ONE OF THE FUNDAMENTAL CONCEPTS OF BAYESIAN STATISTICS IS THAT YOU ARE ABLE TO UPDATE YOUR INITIAL HYPOTHESIS ABOUT HOW A POPULATION OF INTEREST IS DISTRIBUTED BASED ON NEW INFORMATION. THUS, YOU DON’T CONFIRM YOUR PRIORS, YOU UPDATE THEM IN RESPONSE TO INFORMATION DERIVED FROM A SAMPLE (WHICH CONTINUES TO BE A SYSTEMATIC SET OF MEASUREMENTS OF MORE THAN ONE THING AND HAS NOT, NOR HAS IT EVER BEEN, WHAT A JOURNALIST SEES ON A TV SHOW), AND THE EXTENT TO WHICH YOU UPDATE THEM REVEALS INFORMATION ABOUT THE QUALITY OF THOSE INITIAL ASSUMPTIONS. TO STATE THAT YOU ARE CONFIRMING YOUR PRIORS IS TO REVEAL THAT YOU NEVER INTENDED TO REVISIT YOUR ASSUMPTIONS IN THE FIRST PLACE AND ARE INCAPABLE OF REASONING, NOT ENGAGING IN IT.