Are Women Not In Tech For The Same Reason Women Are More Religious Than Men?

I wrote a few posts going over some of the reasons why women are more religious than men. And this is a human universal, like women being shorter than men. Sure, there are some women that are taller than some men, but this is a description about populations, not about individuals.

So for example, in the theory of evolution it’s the populations that evolve, not people (much to the science-illiterate Creationist’s chagrin). A chicken will never give birth to a dinosaur; if you think so, then you don’t understand the difference between populations and individuals.

The Latin language evolved into Italian/French/Spanish/Portuguese. Individual ancient Romans didn’t start speaking Italian one day.

Or for a more irreverent example, refrigerators (as a population) have evolved since the early 20th century. Refrigerators will continue to evolve. Your personal refrigerator does not evolve.

For all intents and purposes, you might as well think of a population as having nothing to do with an individual (population information just gives you a prior probability about an individual). If you’re stuck in traffic, you are not the traffic. Traffic has different behavior than you as an individual.

With that basic statistics lesson out of the way, let’s get on with what I wrote in the title of this blog post. That the reason women are more religious than men is also probably the reason there aren’t more women in tech.

Slate Star Codex:

So let’s look deeper into what prevents women from entering these STEM fields.

Does it happen at the college level? About 20% of high school students taking AP Computer Science are women. The ratio of women graduating from college with computer science degrees is exactly what you would expect from the ratio of women who showed interest in it in high school (the numbers are even lower in Britain, where 8% of high school computer students are girls. So differences exist before the college level, and nothing that happens at the college level – no discriminatory professors, no sexist classmates – change the numbers at all.

Does it happen at the high school level? There’s not a lot of obvious room for discrimination – AP classes are voluntary; students who want to go into them do, and students who don’t want to go into them don’t. There are no prerequisites except basic mathematical competency or other open-access courses. It seems like of the people who voluntarily choose to take AP classes that nobody can stop them from going into, 80% are men and 20% are women, which exactly matches the ratio of each gender that eventually get tech company jobs.

Rather than go through every step individually, I’ll skip to the punch and point out that the same pattern repeats in middle school, elementary school, and about as young as anybody has ever bothered checking. So something produces these differences very early on? What might that be?

Might young women be avoiding computers because they’ve absorbed stereotypes telling them that they’re not smart enough, or that they’re “only for boys”? No. As per Shashaani 1997, “[undergraduate] females strongly agreed with the statement ‘females have as much ability as males when learning to use computers’, and strongly disagreed with the statement ‘studying about computers is more important for men than for women’. On a scale of 1-5, where 5 represents complete certainty in gender equality in computer skills, and 1 completely certainty in inequality, the average woman chooses 4.2; the average male 4.03. This seems to have been true since the very beginning of the age of personal computers: Smith 1986 finds that “there were no significant differences between males and females in their attitudes of efficacy or sense of confidence in ability to use the computer, contrary to expectation…females [showed] stronger beliefs in equity of ability and competencies in use of the computer.” This is a very consistent result and you can find other studies corroborating it in the bibliographies of both papers.

Might girls be worried not by stereotypes about computers themselves, but by stereotypes that girls are bad at math and so can’t succeed in the math-heavy world of computer science? No. About 45% of college math majors are women, compared to (again) only 20% of computer science majors. Undergraduate mathematics itself more-or-less shows gender parity. This can’t be an explanation for the computer results.

Might sexist parents be buying computers for their sons but not their daughters, giving boys a leg up in learning computer skills? In the 80s and 90s, everybody was certain that this was the cause of the gap. Newspapers would tell lurid (and entirely hypothetical) stories of girls sitting down to use a computer when suddenly a boy would show up, push her away, and demand it all to himself. But move forward a few decades and now young girls are more likely to own computers than young boys – with zero little change in the high school computer interest numbers. So that isn’t it either.

So if it happens before middle school, and it’s not stereotypes, what might it be?

One subgroup of women does not display these gender differences at any age. These are women with congenital adrenal hyperplasia, a condition that gives them a more typically-male hormone balance. For a good review, see Gendered Occupational Interests: Prenatal Androgen Effects on Psychological Orientation to Things Versus People. They find that:

Consistent with hormone effects on interests, females with CAH are considerably more interested than are females without CAH in male-typed toys, leisure activities, and occupations, from childhood through adulthood (reviewed in Blakemore et al., 2009; Cohen-Bendahan et al., 2005); adult females with CAH also engage more in male-typed occupations than do females without CAH (Frisén et al., 2009). Male-typed interests of females with CAH are associated with degree of androgen exposure, which can be inferred from genotype or disease characteristics (Berenbaum et al., 2000; Meyer-Bahlburg et al., 2006; Nordenström et al., 2002). Interests of males with CAH are similar to those of males without CAH because both are exposed to high (sex-typical) prenatal androgens and are reared as boys.

Females with CAH do not provide a perfect test of androgen effects on gendered characteristics because they differ from females without CAH in other ways; most notably they have masculinized genitalia that might affect their socialization. But, there is no evidence that parents treat girls with CAH in a more masculine or less feminine way than they treat girls without CAH (Nordenström et al., 2002; Pasterski et al., 2005). Further, some findings from females with CAH have been confirmed in typical individuals whose postnatal behavior has been associated with prenatal hormone levels measured in amniotic fluid. Amniotic testosterone levels were found to correlate positively with parent-reported male-typed play in girls and boys at ages 6 to 10 years (Auyeung et al., 2009).

The psychological mechanism through which androgen affects interests has not been well-investigated, but there is some consensus that sex differences in interests reflect an orientation toward people versus things (Diekman et al., 2010) or similar constructs, such as organic versus inorganic objects (Benbow et al., 2000). The Things-People distinction is, in fact, the major conceptual dimension underlying the measurement of the most widely-used model of occupational interests (Holland, 1973; Prediger, 1982); it has also been used to represent leisure interests (Kerby and Ragan, 2002) and personality (Lippa, 1998).

In their own study, they compare 125 such women and find a Things-People effect size of -0.75 – that is, the difference between CAH women and unaffected women is more than half the difference between men and unaffected women. They write:

The results support the hypothesis that sex differences in occupational interests are due, in part, to prenatal androgen influences on differential orientation to objects versus people. Compared to unaffected females, females with CAH reported more interest in occupations related to Things versus People, and relative positioning on this interest dimension was substantially related to amount of prenatal androgen exposure.

What is this “object vs. people” distinction?

It’s pretty relevant. Meta-analyses have shown a very large (d = 1.18) difference in healthy men and women (ie without CAH) in this domain. It’s traditionally summarized as “men are more interested in things and women are more interested in people”. I would flesh out “things” to include both physical objects like machines as well as complex abstract systems; I’d also add in another finding from those same studies that men are more risk-taking and like danger. And I would flesh out “people” to include communities, talking, helping, babies, children, and animals.

So this theory predicts that men will like jobs with objects, machines, abstract systems, and danger; women will like jobs with people, talking, helping, babies, children, and animals.

Somebody armed with this theory could pretty well predict that women would do well in medicine and law, since both of them involve people, talking, and helping. They would predict that women would dominate veterinary medicine (animals, helping), psychology (people, talking, helping, sometimes children), and education (people, children, helping). Of all the hard sciences, they might expect women to prefer biology (animals). And they might expect men to do best in engineering (objects, machines, abstract systems, sometimes danger) and computer science (machines, abstract systems).

This “people vs. things” distinction looks to explain a lot more of the underlying phenomenon than the “sexism” explanation does. Not only is a focus on people over things probably the reason why there are less women in tech than men, but it probably also explains why women are more religious and participate in religious activities (e.g., praying, going to church) than men do. And this is an extra hit against the sexism explanation because religions are some of the oldest and most sexists institutions that we’ve got going on.

As Scott notes elsewhere in his post, the most sexist and gender inegalitarian societies also have virtual gender parity in tech (so if we want more women in tech, then we’re going to have to model Zimbabwe or 1950s America and take away women’s rights).

What sort of observations does sexism say we shouldn’t see? With sexism, are all things possible? It might provide a good narrative, a story about good vs. evil, but good stories should raise red flags when trying to explain things. Our intuitions about what makes something a good explanation will almost always be rooted in gaining allies.

Note that the women who weren’t more concerned with people over things were women with congenital adrenal hyperplasia. In one of my posts on the possible reasons why women are more religious than men, I noted some hormonal differences between men and women:

I’m willing to bet that, just like people with autism are less religious than neurotypicals, women with CAH are probably less religious than the average woman. 

What’s the takeaway from all of this? That we need to first have an accurate model of the world before we try solving the world’s problems. Trying to solve a problem that doesn’t really exist is the equivalent of getting a surgery when you don’t need one.

The best models of the world are derived from our STEM fields of study. Of course, I work in STEM, in tech, so I might be biased. On the other hand, non-STEM fields, as my previous post intimated, have biases against the scientific method.

Yeah, I know, everyone is biased. Talk about bias is everywhere these days. But… the best way to remove bias is to remove the human element. The best way to root out bias is to shut up and multiply. Having a good story — with clear good guys and clear bad guys — is not how you root out bias; stories hijack our brain firmware and just introduce new biases.

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Posted by on August 8, 2017 in religion


Scholars who believe nurture trumps nature also tend to doubt the scientific method

A survey of more than 600 scholars from 22 disciplines, ranging from psychology and economics through to gender studies, sociology and the humanities, finds that there remain two distinct cultures in the academe, at least regarding views on the principal causes of human behaviour and human culture.

One group, made up of psychologists, economists, philosophers and political scientists believes more strongly in the genetic influences on behaviour, beliefs and culture. The other group, consisting sociologists, non-evolutionary anthropologists, women’s and gender studies scholars and all humanities scholars (except philosophy), believes in the primacy of environmental influences. What’s more, those scholars favouring environmental accounts also tend to be sceptical of the scientific method.


“Human behaviour is not subject to immutable laws, and, therefore, can’t be studied scientifically,” said a religious studies scholar. “Scientific knowledge has something to tell us about material artefacts and their production, but ‘human nature’, ‘human experience’ and ‘human behaviour’ are not empirically stable,” said a literary studies scholar.

In contrast, scholars favouring genetic and evolutionary accounts of behaviour expressed faith in science.


Carroll and his colleagues said “Most researchers who regard human behaviour as beyond the reach of science, or who deny that science has any special claims on the production of knowledge, have more academic respectability that creationists, but they are similar to creationists in that they step willingly outside the circle of knowledge susceptible to empirical falsification.”

Read more at PsyPost


Simpson’s Paradox And The Positive/Negative Effect of Religious Belief

While not necessarily related to Bayes Theorem, something like this has been popping up in my mind whenever I read news stories dealing with statistics so I thought I would make a post about it.

In simplest terms, aggregate data might have different statistical properties than subsets of the aggregate data. As a matter of fact, the aggregate data might show the completely opposite effect when looked at in subsets.

An intuitive example of this is weather. You can average the temperature over the course of the year, or you could find the average of temperature over the course of six months. It might be that temperature over the course of the year has a slightly positive upward slope, yet temperature from June to December has a negative slope.

This seems obvious. But what if you’re dealing with something that’s not so obvious?

The example Wikipedia gives that I think is a non-controversial example is kidney stone treatment. Say you have Treatment A for either large or small kidney stones and Treatment B for large or small kidney stones.

Treatment A is effective on 81 out of 87 (93%) small kidney stones while Treatment B is effective on 87% (234/270) small kidney stones. For large kidney stones, Treatment A is effective 73% (192/263) of the time and Treatment B is effective 69% (55/80) of the time.

Clearly, Treatment A is what you should use for both small and large kidney stones. But what happens when we aggregate over both small and large kidney stones? Treatment A is 81/87 + 192/263 = 273/350 (78%) while Treatment B is 234/270 + 55/80 = 289/350 (83%). Now it turns out that Treatment B is better than Treatment A!

Therein lies Simpson’s Paradox. What happens when we have something controversial? Wikipedia also has the example of apparent sexism in graduate school admissions (which it still seems like no one has tried to account for this paradox when talking about modern controversies like the gender wage gap). But this is mainly a religion blog: So what about whether religion is good or bad for people or society?

Very religious Americans […] have high overall wellbeing, leading healthier lives, and are less likely to have ever been diagnosed with depression… These positive associations between religious engagement and the good life are reverse when comparing more versus less religious places rather than individuals…

Gallup World Poll data from 152 countries [show] a striking negative correlation between these countries’ population percentages declaring that religion is “important in your daily life” and their average life satisfaction score…

Across US states, religious attendance rates predict modestly lower emotional well-being…

Epidemiological studies reveal that religious engagement predicted longer life expectancy…

Across states, religious engagement predicts shorter life expectancy…

Across states religious engagement predicts higher crime rates. But across individuals, it predicts lower crime rates…

If you want to make religion look good, cite individual data. If you want to make it look bad, cite aggregate data…

Stunning individual versus aggregate paradoxes appear in other realms as well. Low-income states and high-income individuals have [recently] voted Republican…

Liberal countries and conservative individuals express greater well-being…

Highly religious states, and less religious individuals, do more Google “sex” searching…

One might wonder if the religiosity-happiness association is mediated by income — which has some association with happiness. But though richer people are happier than poor people, religiously engaged individuals tend to have lower incomes — despite which, they express greater happiness.

This is from a conference paper. I’m not actually sure if this is an example of Simpson’s Paradox, but the larger point remains. Breaking up data along different axes might yield paradoxical results. As the author says, if you want to make religion look bad, cite aggregate data. If you want to make religion look good, cite individual data.

But which statistic should one use? The aggregate data or the individual data? They’re both true, for lack of a better word, so it’s not like one is “lying”. I would tend to lean towards using the aggregate data if forced to choose. But there’s no harm in looking at both. And if both paint the same picture that just means that you have a more complete view of the phenomenon at hand.

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Posted by on June 26, 2017 in Bayes, economics/sociology, religion


People on the left and right use different cognitive systems to make moral judgments

“When it comes to emotionally charged, morally contentious decisions, people who self-identify as conservatives tend to make these decisions based on their emotive, intuitive responses to the scenario in question (their gut feelings), whereas those who identify as liberal tend to put gut-feelings aside and make decisions based on an attempt to reason logically and consciously about the scenario,”

Read more at PsyPost

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Posted by on June 26, 2017 in cognitive science


Partisan Bias in Factual Beliefs About Politics


Bullock, et al., 2013:


(h/t Bryan Caplan

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Posted by on June 9, 2017 in religion


Study uncovers how brain damage increases religious fundamentalism



Read more at PsyPost

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Posted by on June 8, 2017 in cognitive science, religion




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Posted by on June 6, 2017 in economics/sociology

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