"Type I" and "Type II" errors, names first given by Jerzy Neyman and Egon Pearson to describe rejecting a null hypothesis when it's true and accepting one when it's not, are too vague for stat newcomers (and in general). This is better. [via]
via flowingdata.com
To this day I must think hard to figure out Type I and II errors.
Hat tip: Jayjit Roy
