By Thoreau
John Tierney has decided to get himself some attention by writing about the hypothesis that gender differences in science careers can be explained by the variance in mathematical ability distributions being wider among men than women. This hypothesis is almost certainly wrong*, but not for some of the reasons often offered. Remember when I took on the race/IQ theorists, and noted that their detractors often over-simplify and thus weaken the pro-equity case? Well, the detractors of the variability hypothesis do something similar. Generally they sum up the hypothesis as “Women are bad at math.” This is an over-simplification of a wrong idea. The problem with the over-simplification is that it then gives the purveyor of the wrong idea the satisfaction of being able to sit back and say “That’s not what I said” and conclude that his opponents are driven by politics rather than reason. I don’t want to give them that satisfaction, so I’m going to present the variability hypothesis in its least bad form and then attack it.
First, the hypothesis is not a blanket statement about women and men, suggesting that every woman is deficient in mathematical ability and every man has an inherent biological advantage. Most of the opponents of Tierney and Summers get that, but they still sum it up as “Women are bad at math.” It is instead a statement about distributions, looking at the number of women and men at each level. The hypothesis is that women tend to be closer to average, with fewer above average but also fewer below average, while men are more likely to have outliers on the high and low sides, with more men at the bottom of the math class and at the top. Some framers of this hypothesis even suggest that women might actually have a slightly higher average, but because fewer women deviate significantly from their average there will still be fewer women at the top of the mathematical game.
Overall, this is a hypothesis that is only favorable to men at the highest levels. Unfortunately for the framers, it’s also a hypothesis whose statistical support is questionable at best. However, I’ll let people who are more immersed in the statistics take up that issue. Instead, I’ll focus on the “So what?” question: Even if this hypothesis were (for the sake of argument) correct, does it have any use in explaining disparities in scientific careers? I’ll argue “no” for 3 reasons:
1) This hypothesis is, if true, most applicable at the far ends of the distribution. However, your average college science or engineering classroom isn’t drawn from the very top of the bell curve. Not every science or engineering department is at Caltech or MIT, alas. You can go to a science or engineering class at a substantially less selective university and still see a substantial disparity. If we see this disparity in a group that is closer to the center of the bell curve, you can’t attribute it to the variance hypothesis. You have to attribute it to other factors. And since we have seen changes in the demographics of science and engineering over time, and since those changes have coincided with social changes, social variables are a far more plausible explanation than biological ones.
2) At the upper end, innate intellectual aptitude for abstract reasoning is certainly part of the game. So is creativity. So is determination. So is luck, or, more precisely, the ability to seize on good luck when it happens while riding out bad luck (more of a personality trait than an intellectual trait, in the phrasing of this non-expert). So is communication ability (since getting ahead in basic research or industry is in part about persuading people that your approaches are good). And a host of other traits.
Certainly raw talent for math is part of it, but hardly the only part. The highest echelons of science and engineering are dominated by people who have the total package, and no two people have the exact same mix in their packages. Some are weaker on one part than another, but they bring enough of each to the game, and they use what they bring, and they get where they go. A great American engineer and scientist spoke of 99% perspiration and 1% inspiration. Which is not to say that smarts don’t matter, but that a person could be slightly closer to the center of the bell curve by some measure of smarts and still win by perspiration.
So, knowing how a particular trait is distributed tells you little about how many people have the complete package. It would be like looking at manual dexterity to the exclusion of all other factors, and trying to predict a person’s career path. The most dextrous person around may very well become a heart surgeon. Or he may become an auto mechanic. Or an engineer. Or a sculptor. Some of those paths have far more money and prestige than others, but looking solely at manual ability would tell you little about that person’s ability to go far and rise in the world. You’d have to look at the total package. In fact, even if you looked at a room full of medical students, manual dexterity might tell you who becomes a surgeon, but it wouldn’t tell you who becomes a famous surgeon. Or who makes the most money. The one who goes down the R&D path might become famous for an invention, but the one who goes into plastic surgery and makes the right connections in high society might make 10x as much money. And the one who is just really, really good with his hands but not very inventive or charismatic might go into a less lucrative specialty and never become famous at all, despite having the best hands in the entire class.
The bottom line is that even if (for the sake of argument) this hypothesis regarding the extremes of the distribution is correct for one particular trait, it tells us little about how many people of each gender will be able to succeed in science and engineering. And it tells us nothing about the vast majority of people trained in science and engineering, who are smart but not on the extremes.
*By writing “almost” in front of “certainly” in a post on a provocative topic I do run the risk of being paraphrased as “I absolutely cannot rule out…” Let’s try to avoid that.