Trouble Thinking

June 8, 2011

Shortcut Science: Being Controversial Rather Than Replicable

So about a week and a half ago, someone named Satoshi Kanazawa published an article that was called “Why Are Black Women Rated Less Physically Attractive Than Other Women, But Black Men Are Rated Better Looking Than Other Men” in his blog on Psychology Today called “The Scientific Fundamentalist”. From a brief look at the blog, it’s mostly focused on various attention-grabbing BS claims like “Beautiful People Really Are More Intelligent” and basically anything that is easy to defend because it’s a “hard truth” that just happens to track perfectly with common sense and the fears and cultural baggage of the average person. I haven’t checked but “A Bigger Penis Makes You A Better Person” is probably in there. In fact, the tagline to the blog is “A Look at Hard Truths About Human Nature”.

Any time anyone tells you they’re going to give you a Hard Truth and Not Be PC they are trying as hard as they can to explain that with no bias they’ve discovered everything is exactly the way their gut intuition has always told them it was and woah woah woah don’t go there this is just what Science or Common Sense or Common Science says not them. The fact that their Not Being PC or Being Tough happens to conform precisely to the prejudiced notions they hold is a happy accident.

I’ve heard this so many times in so many different contexts and it’s always infuriating. “IQ testing is completely right, even if they don’t want me to talk about it because it will make people sad to find out that certain people [who happen to happen to be from a group that I don’t belong to] are inherently dumber!”

Not “I have spent a lot of time devising a clever and replicable experimental proof of this interesting aspect of human nature.” Just stating it’s controversial, as though people being unreasonably opposed to a potential experimental proof that you’re too lazy to actually provide is somehow an airtight case.

Oh, and in case you’re wondering, no there is not actually any reasonable way to interpret the data Mr. Kanazawa gathered as indicating that black women are rated as less attractive “due to testosterone” as he winds up concluding. Not that he gives any actual reasons as to why testosterone would create a less attractive person. He basically seems to just imply that black women are mannish and black men are hyper-male, and woah isn’t it weird how that just happens to reinforce long standing prejudices I dunno maybe you just can’t handle the truth!

From a Scientific American article that’s way more thorough than me, damn:

Kaufman and other bloggers also address Kanazawa’s painful contortion of factor analysis, which I agree is laughable. He looks at three measurements of the same test taken at three different time points and creates a one-factor model, with the one factor being “objective attractiveness.” This is, of course, founded on the principle that an attractiveness rating handed out by interviewers in a study on adolescent health and well-being is actually measuring something that we can agree is “objective attractiveness.”

He then says that by merging these three measurements for each interviewee into one factor, he can use factor analysis to get at that “objective attractiveness” while minimizing any error. This is just plain false. Factor analysis cannot get rid of measurement error. If it could, we’d all be using it all the time, and we’d get rid of all measurement error, and scientific studies wouldn’t need to be replicated.

Basically, the jackass claimed that through a reasonably simple statistical trick, factor analysis, he’d managed to completely eliminate any error, and therefore get “objective” attractiveness. That’s not what factor analysis is for. Factor analysis is a method of testing for the likelihood of a lot of different observed variables being explained by a single unobserved one. So like, if you notice that during the summer you sweat more, wear fewer clothes, and use more energy all of those things could be explained by different variables, or they could all be related to things being generally hotter. Usually factor analysis is used to take a bunch of random variables you’ve gathered and just poke around to see if there’s a simpler explanation for the changes in each of them.

Notice at no point does factor analysis magically transform things into objective measurements of subjective human experiences. So his basic method of working toward a conclusion is just straight up stupid from the get-go.

Then there’s the conclusion itself. It turns out that actually, black women may have lower than normal levels of testosterone on average, from the same article:

Kanazawa surmises that Black women’s lower attractiveness might be due to low estrogen and high testosterone. Yet, high estrogen levels and low testosterone is a leading cause of fibroids, which significantly impact Black women, especially Black women who are overweight. Also, Black women have been found to have higher levels of estrogen in a study on breast cancer.

Oh, and it turns out that during the 4th out of four measurement phases, in which the participants were adults, black women were not rated as less attractive. He threw that out though, because well I mean it’s an outlier! Or something.

I mean, I could go on (read the rest of that Scientific American article for even more shit about this) but the underlying factor is what’s important here. This article got a lot of press for being both exceptionally stupid and exceptionally controversial, but this whole method of not-investigation needs to be let go. If your work is controversial, it’ll be controversial. If you make replicable experiments and provide useful avenues of exploration eventually people will come around and learn something interesting no matter how insane they thought it was at first. The truth will out.

If you chase the controversy that comes with truth instead of actual understanding, you wind up like this jackass.

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