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by rayiner 4597 days ago
The problem with his argument is not the statistics, which are sound, but the fact that he begs the question and also ignores some obvious implications of the very data he presents.

First, let's talk about mathematical ability. It's widely known that men outnumber women in the upper percentiles of mathematical ability. However, that's an explanation for why there are so few female Fields Medalists (in fact, there are none), not why there are so few female engineers.

Among people who score a perfect 800 on the Math SAT (top 1% starts at 770), men outnumber women only 2:1. Even if mathematical ability is totally determinative, and being a programmer required top 0.3-0.5% of mathematical ability, we would expect to see ratios of maybe 65/35 in the programming world, not 90/10 or 95/5. Due to the shapes of the bell curves in question, the disparity between men and women gets quite large when you get into the 0.1% or 0.01% of mathematical ability. But, by and large, Silicon Valley isn't made up of those people. They're more run of the mill smart people (Stanford's SAT Math inter-quartiles are 93rd-99th percentile).

With regards to the points about competitiveness versus cooperation and risk-taking and caring about people, they all beg the question. Why is competitiveness a good thing for the business of writing software? Don't you think cooperation would be better for such a deeply team-oriented discipline? Why isn't caring about people a positive strength, when much of Silicon Valley 2.0 is fluffy social stuff? Finally, while more risk-aversion might explain why there are fewer female founders, it doesn't seem to be the case that females are less represented in startups than in technology companies in general. What's risky about going to work at Microsoft or Google?

The refrain of "these statistics are things nobody is willing to talk about!" is a cop-out. Most people will not pillory you for pointing out that women are more risk-averse or do things differently. Indeed, it's something women themselves often talk about. My wife was recently at a social gathering for women attorneys. She recounted a discussion of how women tend to disclose when they haven't done something before, while men tend to say "sure I can do that." It's not 1990 and people are quite willing to discuss how men and women approach work differently. But the statistics only support conclusions as strong as the scope of the evidence. And in this article, the author wanders far beyond what the statistics support into blatant conjecture and rationalization.

4 comments

It could be the butterfly effect. It starts with children. Better at math -> more interested in math -> better at math -> ... by they time young adults start seeking jobs, a small initial bias has turned into a large gap.

Personally, I don't think that's the whole story (according to my observations, it's also upbringing, girls socialize more than boys, who are more likely to keep to themselves, and differences in topics of interest, which I have no idea what they come from).

First, it's probably not the case that interest and practice can have any impact on your underlying mathematical aptitude. Second, regardless, the 2:1 ratio for perfect scores on the SAT Math manifests at 16-18, so when people are already mostly developed.
> First, it's probably not the case that interest and practice can have any impact on your underlying mathematical aptitude.

What?! How did you arrive to that conclusion? This is a really bad myth. Math abilities for the most part have very little to do with genes, and almost all to do with hard word, motivation, and practice. See http://www.theatlantic.com/education/archive/2013/10/the-myt... for more.

No ,I was saying that they have an impact on the actual mathematical aptitude, and the (again actual) ratios of men/women in STEM industries.
>>Why is competitiveness a good thing for the business of writing software? Don't you think cooperation would be better for such a deeply team-oriented discipline?

Mostly because compensation, benefits, raises, promotions and incentives are in most cases at individual levels. So people chase goals what you want them to chase.

Also note what's good for the team is often not good for the individual. I know this from personal experience, if you have a mix of high and low performers in a team and the rewards for them are going to be the same regardless of performance- The next thing that happens is the low performers make no attempt to increase their performance and high performers are always supposed to make up for it.

This creates immense frustration for people who are performing well. They see no reason why they must be doing heroics to take the same amount of money as the guy who isn't putting even a fraction of their effort. So the whole team collaboration stuff collapses.

Even if mathematical ability is totally determinative, and being a programmer required top 0.3-0.5% of mathematical ability, we would expect to see ratios of maybe 65/35 in the programming world, not 90/10 or 95/5.

That's true. But he described multiple factors that bias men towards programming. Even if individual factors (like mathematical ability) only cause 65/35 differences in isolation, combining a few of them together results in pretty extreme distributions.

The difference in mathematical ability is about the starkest difference between the aptitudes of men and women. You expect a 65/35 difference if mathematical aptitude is the only thing that matters, and if a top 0.5% level of mathematical aptitude is required for programming. If mathematical aptitude is say only 40% of what makes a good programmer, and the factors in the other 60% skew less than mathematical aptitude, then you'd expect a more even ratio than 65/35. Thus, consideration of other factors dilutes the analysis.
Let's say being a programmer requires X, Y, and Z. X is 65/35 male/female, Y is 60/40, Z is 55/45.

The odds of finding a woman with X, Y, and Z is 0.35 * .4 * 0.45, about 5%.

The odds of finding a man with X, Y, and Z is 0.65 * .6 * 0.55, about 20%.

All else equal, you'd find that about 80% of programmers are male.

Obviously my example is contrived, but if you think I'm wildly wrong, please respond with something math-based, even if it's equally contrived. Then we'll at least know what the other is really getting at.

Your math obviously doesn't make any sense, since say a 65/35 representation of men versus women among people who score a perfect Math SAT doesn't mean there is a 65% probability of finding a man with that characteristic and a 35% probability of finding a woman with that characteristic.

I was thinking more like this: consider giving everyone a goodness score. At first, let's say the goodness score is entirely based on mathematical ability, which skews strongly in favor of men. Then, let's make the goodness score based 50% on mathematical ability, and 50% based on an independent criterion that skews in favor of men, but less strongly. There should be more women who achieve a certain goodness score under the second set of criteria than the first. That's what I mean when I say that considering additional criteria is dilutive.

Besides that, all your metrics skew in favor of men. But out of the various metrics that go into being a good programmer, I think mathematical ability is the only one that skews in favor of men. Studies show that female students get higher grades, because they are more attentive over longer periods, have more patience and impulse control. Also, in terms of programming teams, I think being social and communicative is far more of an asset than being competitive.

>Among people who score a perfect 800 on the Math SAT (top 1% starts at 770), men outnumber women only 2:1

So you are selecting outliers to justify the "proper" average outnumbering? That doesn't add up.

The shape of the curves are such that men have a very small advantage on the Math SAT at the median, which gets larger up the scale. So using the disparity between the sexes at the median makes the author's argument even worse.
Even being 2:1 why would you expect those results being reflected anywhere else? Why would an employer hire from one of the two pools of people where one haves 50% less chance of being good enough for the position?
If an employer just hired people with perfect SAT math scores, you'd expect a 2:1 distribution. If an employer just hired men, because they were twice as likely to have a high SAT math score, then the employer would be retarded because for both genders there is over a 99% chance that any given person will not have a high SAT math score.
Not a "high" but a better, and that's usually enough; and the gap seems to be increasing[0].

[0]http://www.aei-ideas.org/2012/09/2012-sat-test-results-a-hug...