Yow! First all statistics are wrong, then all statistics that involve percentages of people are wrong, then statistics that come from evil people are even more wrong, and finally most statistics are more likely to be completely wrong than partially right. Does this mean that Derek Jeter *didn't* bat .339 this past season?

Ok, there's a really good point behind all this that I think I ought to address before I succumb to the urge to run amuck and node All Rhetoric is Lies!, All Fiction is Lies!, and All Opinion is Lies!.

Freshman English classes often focus on rhetoric. They attempt to teach what makes a good argument, what makes good evidence, what's a persuasive way to build an argument, and so on. Statistics aren't much different from rhetoric. They're a form of quantitative argument built on top of probability theory. In English class, they teach you how to differentiate good rhetoric from bad rhetoric, and in statistics class (well, advanced ones anyway) they teach you how to differentiate good statistics from bad statistics.

In rhetoric you have evidence and in statistics you have data. If your evidence is dubious or your data are crap, so are your results. This is the point that Cletus focused on when he described surveying techniques. Most of the statistics that people are familiar with come from surveys and, as Cletus pointed out, there are gobs of ways to do it badly. Since popular publications omit methodology (Who the hell wants to know how a survey was done? Gimme the results, dammit!), it's unlikely you'll be able to catch any problems here.

In rhetoric you have logical fallacies (click on it to see a great metanode) and in statistics you have statistical fallacies. The statement "Never trust the truth coming from an evil person" is an incitement to *argumentum ad hominem*. When a researcher fits a line to nonlinear data, he's abusing linear regression. Again, popular publications tend to omit such details, so you won't be able to catch mistakes here.

Statistics allows an additional pitfall: misinterpretation. Seeing a correlation and assuming cause-and-effect is a big example. In *The Mismeasure of Man*, Gould cites another one, namely ascribing physical substance to the results of factor analysis. Luckily, you can sometimes catch this kind of thing in since it's the point where a writer states a statistic them jumps to a conclusion.

To me, the lesson is that statistics are often misused (either intentionally *or* accidentally) in making a point and that without knowing *how* those statistics were created, you can't say whether they support the argument, in exactly the same way that the statment "Most statistics...have been tampered with..." doesn't give any clue as to whether it was revealed by God or just a guess.