In his defense (and admitting your points are all completely right): I consider my self analytically minded (i hope I am to some degree since its how I make my

In his defense (and admitting your points are all completely right): I consider my self analytically minded (i hope I am to some degree since its how I make my living). Statistics during university also gave me a headache. Still does. 10 years later though: I've now worked for the national statistics body. I put my head down and said "Screw this, every time I don't get something, I'm going to try to implement it from first principals until I do. I'm going to code it up. Every time I don't understand the paradigm/language, i'm going to try to do everything in that mindset until I do." I worked for several years in the stats methodology division until I moved on. I am now of the not uncontroversial opinion admittedly, but ever increasing in popularity, that the reason I didn't understand stats in university stems from: 1) It is taught badly. 2) Often what is taught IS wrong. 3) In practice it is used wrong: sig testing is the most overused technique I've seen in any field i've had experience with, except perhaps linear models (another stats baby) and in my experience, once one has internalised the lessons/mindset of variance/sampling/etc rather than "correct/not-correct", statistical significance is almost always tangential to the actual analytical question, yet it is often treated as the goal. I think its popularity is partly due to the fact that it does provide someone a framework for yes/no decision making, negating the very mindset change needed to properly understand statistics. Frequentist techniques were the focus in university stat level classes, and such a framework and models are often forced into very non-frequentist type situations where a bayesian/subjective interpretation is, in my subjective opinion, more rational and justified. My stats classes had almost no material on computation, logic, falsifiability, experiment and data design, etc, which, subjectively I view as much more important to real statistical work than rote learning what a regression, least squares, or R^2 measure is. A great deal of the time, analysts have trouble explaining the methods they use because its obvious (to me) that they don't really understand the methods they're using (if they did, they often wouldn't be using them).