Stats For Poets
I have decided to take a refresher course in quantitative methods, designed for legal academics and lawyers. It's designed to be a basic intro to multiple regression analysis and to teach me how to use STATA.
The last time I took a statistics course I did pretty well, but it was seven years ago, from Fall 2000-Spring 2001. I was a junior in college, and while I could have taken three quarters of linguistics to satisfy the math requirement had I only been an English literature major, as a double political science major I had to at least take statistics. So, that I did. And it was good and interesting, and I learned how to use SPSS. And because I have forgotten how to do statistical analysis, it is time for a refresher.
The professors tried to kick me out of class. Me and everyone else who has taken statistics. This stats course is quite basic and introductory: while I may have a hard time remembering what a T-test is vs. a p-value, or the difference between one-tail or two-tailed test, or what a chi-square test is, at least I know these terms. Supposedly, this class is for true neophytes. This is why this post is called "Stats for Poets," you would presume that poets (cough) do not know statistics, but had I titled this "Stats for Lawyers," some of you may have been offended by the implication. You know, the implication being that the coursebook for such a course could be hazard sign yellow with a black chalkboard sign saying that you are a dummy.
Although appallingly, most law students and lawyers have never taken a statistics course, and statistics are being increasingly used in litigation and scholarship. That's my reasoning for re-taking something I'm not that much of a dummy about. I know that it'll all come flooding back to me fairly quickly, but I want to be fluent enough in basic statistics such that next Fall, when I take an advanced statistics course and maybe try to learn how to design my own models and experiments, I won't fail that course. Reading tables where the p-values are bolded for you and where they telly ou the threshhold of significance isn't that hard. Understanding what that really means doesn't require much training. But designing your own research does require training, and it's a process that requires the cumulative accretion of knowledge and skills. Understanding the purpose of such research in addition to mechanical aspects of executing such research.
So, that's why I'm staying in this intro course, and why I'll be doing problem sets and going to the lab every week to plug numbers into computers, and why I'm going back to college.