4 ms·
I think most people working with hypotheses and theories should read this. All too often I see authoritative figures dismiss presented evidence because their t
by hnfong 3y ago
I think most people working with hypotheses and theories should read this.
All too often I see authoritative figures dismiss presented evidence because their theories suggest it is (close to) impossible. It often goes like this:
A: "Hi my name is Mark Xu"
B: "Our models say it's practically impossible that this is true, so it must be an unfortunate glitch, or you are a liar. You don't have a PhD, so I guess I'll assume the latter"
Later...
"Despite rumors that the person Mark Xu has been spotted near South Park, Colorado, scientists say there is no evidence to suggest he really exists. Xu continues to be a mystery confounding scientists till this day...."
- stavros 3y agoSure, but if the odds of someone's name being Mark Xu are one in a million, the odds of someone seeing Bigfoot (or whatever) are many orders of magnitude lower than that.
- hnfong 3y agoAt some point the priors of any theory being wrong is probably more than one in a million. In fact, we know as a historical fact that our current scientific theories are likely to be wrong or inaccurate. Your priors might be different from mine, but positing that odds of Bigfoot (or whatever) is less than one in a trillion or quadrillion isn't that useful.
- epgui 3y agoI think you are mischaracterizing both the nature of Bayesian probability updates, and also the foundation underlying scientific expertise. In the first case, prior probabilities are more often than not weakly informative. In the latter case, experts usually rely on a huge volume of information, beyond what most laypeople could even imagine.