Unlearning Descriptive Statistics explains many things you should know about working with Numbers that your Statistics Class in University probably did not explain properly.
If they did, maybe Graphite would not hurt so much, with all the Averaging going on where it shouldn’t, and maybe Gill Tene would not have had to give talks like How NOT to measure latency (which is awesome, by the way and if you haven’t seen this talk, do it right now).
From the Intro of Unlearning:
If you’ve ever used an arithmetic mean, a Pearson correlation or a standard deviation to describe a dataset, I’m writing this for you. Better numbers exist to summarize location, association and spread: numbers that are easier to interpret and that don’t act up with wonky data and outliers.
Statistics professors tend to gloss over basic descriptive statistics because they want to spend as much time as possible on margins of error and t-tests and regression. Fair enough, but the result is that it’s easier to find a machine learning expert than someone who can talk about numbers. Forget what you think you know about descriptives and let me give you a whirlwind tour of the real stuff.
Go, read the rest.