The Arse-Cheeks of Statistics, and Titillating one's Smart-Parts

[This post is brought to you by me being grumpy about Epidemiology: A Very Short Introduction.]

One of the reasons I went off popular non-fiction a couple of years ago was that it got repetitive. There are only so many times you can read an introductory explanation of Prisoner's Dilemma without wanting to chew up the pages and spray them like confetti out of the window. It makes me wonder how many pages I've read, redundantly reintroducing me to something I know already. How many books do they all add up to?

I can forgive something like Prisoner's Dilemma because game theory is slightly esoteric, but I seem to have a lot less patience when it happens with statistics. I have lost count of the number of places where I've been "introduced" to Pearson's r, or regression to the mean, or sampling biases. My dear host, we've already met, and I know them well. Very well. In fact, I can tell you what kind of underwear they prefer and which songs they end up singing when they're drunk at 3 o'clock on a Saturday morning.

It's not just me. A lot of people are very friendly with introductory statistical concepts. It is not a shy discipline. Anyone who's completed a quantitative research methods course (the majority of science graduates?) has seen at least one arse-cheek of statistics. This is a fairly standard and cross-domain body of knowledge, so let's not be coy.

I assume authors don't want to spend their time torturing metaphors for linear regression either. Nate Silver must prefer to state facts about his material in the obvious language for doing so. I can't help but feel that there's the opportunity for a much better class of popular science writing, if we could just nudge the general public's statistical literacy up a couple of notches.

This would possibly seem a bit elitist as recently as ten years ago, but there are so many resources for gaining statistical literacy in 2014. If you're in a position to read Freakonomics, you're probably in a position to take one of the dozens of MOOCs on introductory stats, or to pick up the quite fine Cartoon Guide to Statistics.  If everyone learned this once, we could factor out hundreds of pages of insipid statistics in all our future reading.

In my ideal world, authors should be able to stick a logo on the front of their book. Maybe a nice red bell-curve. That logo will say:

To read this book you should know, more or less, what a probability distribution is. You should understand measures of dispersion and central tendency. You should understand logs and e. You should know what 'population' and 'sample' are referring to in context. 'Significance testing', 'statistical power' and 'regression' should not be alien terms to you. It would be nice if you had some idea what 'Bayesian' means. We trust that if you don't know a particular piece of terminology, you can look it up on Wikipedia. This book is not just here to titillate your smart-parts. It's here to teach you something.

I think a lot of people read pop-sci books to feel smart, and to signal their smartness to other people. If you make those little red logos an intellectual status symbol, you're going to seriously put the boot up the level of public discourse.