How we think about probabilities...

From Nate Silver's article...

For various reasons, ranging from p-hacking, to survivorship bias, to using normal distributions in cases where they aren’t appropriate, to treating events as being independent from one another when they aren’t — that latter one was a big problem with election models that underestimated Trump’s chances — people designing statistical models tend to underestimate tail probabilities (when probabilities are close to zero, but not exactly zero). Perhaps not coincidentally, people also tend to underestimate these probabilities when they don’t use statistical models. It’s not always the case, but it’s often true that when a supposedly 2 percent or 0.2 percent or 0.02 percent event occurs, the “real” probability was higher — perhaps even an order of magnitude higher. Maybe an ostensibly 1-in-500 event was really a 1-in-50 event, for instance.