This is a fantastic thread https://twitter.com/nntaleb/status/1163192701472428032?lang=en https://twitter.com/nntaleb/status/1163192701472428032?lang=... from T

This is a fantastic thread https://twitter.com/nntaleb/status/1163192701472428032?lang=en https://twitter.com/nntaleb/status/1163192701472428032?lang=... from Taleb. As always, a little bit over the top, but mostly on point. I've seen the exact phenomenon he describes play out so many times now its not even funny. I'm doing a PhD in mathematical statistics, which is sort of like "calculus on steroids". I basically do calculus day in day out. Many of the problems we attack are simply not of much interest to mathematicians. I used to run my problems by math profs & they would say something like yeah it can be done, have you tried mathematica etc. instead of buckling down to do it. For instance, yesterday I had to prove that Fisher information of Cauchy is half. Now that's entirely calculus. You take a function f(x,t) = (pi*(1+(x-t)^2))^-1. You then take the log of that. Then you differentiate that w.r.t t. What you get is called the Fisher score function S(t). So you take the score function and differentiate that again. Lets call that g(x,t). Stick a negative sign in front of that. So now you have a complicated looking new function -g(x,t). You multiply the -g(x,t) with your original f(x,t) and integrate that product over the reals. The result is half. Most mathematicians usually get stuck on that last step. But for (mathematical) statisticians, this os sort of our bread and butter integral. So we know a bunch of tricks. Here's one such trick - https://stats.stackexchange.com/questions/145017/cauchy-distribution-likelihood-and-fisher-information#comment277842_145050 https://stats.stackexchange.com/questions/145017/cauchy-dist... Its like riding a bicycle. Those who ride the most know how to ride. But there are some who want to know how do bikes actually work...which is not going to help you much with riding the bike.