Probabilities under normal curves or any shape probability distribution function are measured as areas under the curve. It helps to have an integral calculus intuition for comparing p(0.1<x<0.5) to p(0.5<x<0.6). It helps to have a multi variate vector distance interpretation of length for error and variance magnitude.
> Yes, a statistics course is so much more useful. It's not emphasized in school but it will truly help through out your life if you understand it.
I think this may be based on an impression of what math coursework used to be. A statistics course is a very common, if not required, part of any modern mathematics major.
yes exactly, and usually it comes after calculus or in parallel.
It provide foundation to work with the data, filling the gaps, or do a first pass on the distribution without fucking it up, and then do some stats on it.
There are a lot of people in these comments saying, "this thing is more important than calculus" and it turns out it's a concept that is fully fleshed out in analysis which is just calculus essentially. I feel like the problem is calculus as taught focuses too much on algrebraic manipulation which is only useful essentially if you become theoretical physicist and little else while the "why" behind calculus leads you to a lot of more useful results that are along the lines of approximation and optimization, which is closer to what a modern understand of analysis is.
I learned it as part of "operational research", that was some algo-y math course. No idea if that translate.
But yeah, definitely closer to calculus than stats or proba.
I think the argument was that a calculus based approach to understanding those subjects dramatically improves and enriches the study of those subjects.
Or even linear algebra. I think it made me better at grasping highly formal stuff.