I don’t want to write this, but I’m being forced at gunpoint. I would never willingly spew such hateful sentiment and espouse ideas as dangerous as the following without threat of violence or death. You know me, I’m a Good Man. Anyway, this will explain why men dominate STEM (science, technology, engineering, and mathematics) fields and why your dumb.

It is the really incredibly very humble Opinion of the Writer that one can reason their way through this and come to the same conclusion as they would from reading The Literature™. If we’re thoughtful enough we don’t need superfluous studies and meta-analyses to back us up, but I suppose it would only help. My two questions before we begin. Sit and ponder for one minute: what exactly is improved by having a 50/50 split of men/women? Why does the gender of the employee matter if the work is done the same?

The Principle of Parsimony

Before we begin, a quick but extremely difficult thought experiment. Let us pretend that five basketball players are playing a game of basketball against another team of five basketball players. I know, pretty far out, but stick with me. The Red team wins game one, but the Blue team wins game two, an even split. They reconvene tomorrow for two more games and do this for four weeks. 28 multiplied by two is 56. They will play 56 games in 28 days. Here’s where it will get tricky.

The Red team won both games several times and won 40 of the 56 (71.4 percent) games played over the four-week span. What is the most obvious and simplest conclusion we can make after 56 games?

  1. Every member of the Blue team lost a loved one at some point in the series and was unable to properly focus on the following few games because of their mourning.
  2. The Red team devised a plan to sneak a minor, unnoticeable cheat into the first two games and in every consecutive game increased the severity of the cheat ever-so-slightly, making them Wall Street-level frauds by the end of the series.
  3. Basketball is dominated by a corrupt hegemony with a long history of oppression. Red teams have historically been allowed to do what they please when they please, often at the expense of Blue teams. This has become so deeply ingrained into the game that fans, players, and team personnel are blind to the tyranny.
  4. The Red team is better than the Blue team at basketball.

Solution: 4. The Red team played the same game as the Blue team every day and won a majority of the time. Remember when the modern world revered the Golden State Warriors for winning 73 of their 82 (89 percent) regular-season games in the 2015-16 NBA season?

Mind the Gap

As of 2012, according to the National Center for Education Statistics (NCES) of all the countries that matter, women earned the following percentages of degrees. Green means that women are the majority, which is an unalloyed good. Red means majority men, which is the verybadnogood problem we are addressing.

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The controversy should stop here, as women make up almost 6 out of 10 graduates. If they are the majority of all graduates there are obviously no barriers to entry or collegiate glass ceilings keeping them from achieving what they desire.

Women are the majority in education, health, welfare, humanities, arts, life sciences, social sciences, business, law, agriculture, personal, transport, environmental protection, and security service fields. On the other hand, men are the majority in mathematics, statistics, physical sciences, engineering, manufacturing, construction, and computer science. Women dominate 16 fields v. men’s 7.

The obvious question of the Rational Reader is, why are men silent about their minority status in non-STEM fields?

Equalitize Me, Captain!

A study was published in February of 2018 by Stoet & Geary that opened with the sentence, “the underrepresentation of girls and women in science, technology, engineering, and mathematics (STEM) fields is a worldwide phenomenon.” For a reasonable person, this would conclude the issue. Oh, it exists across the globe, it has that in common with gravity. But apparently, the gender gap in STEM is still a problem that needs solving. At my alma mater, there are Women In STEM posters all over as if a history major with a short haircut will glance at one and register for Calculus.

The Programme for International Student Assessment (PISA) is the largest educational survey currently conducted by humans. It assesses science literacy, reading comprehension, and mathematics every three years and compares each student against the average score across the three domains. Essentially, it gives the kids a ranking of their strengths and weaknesses while allowing us to get a glimpse into education trends amongst teenagers. In the last study, a whopping 472,242 students from 67 nations or regions were included.

Computer: Enhance

Girls outperformed boys in 28.4 percent (19/67) of countries, boys outperformed girls in 32.8 percent (22/67) of countries, and there was no significant difference in the remaining 38.8 percent (26/67) of countries.

In every country save Lebanon and Romania (97 percent), “boys’ intraindividual strength in science was (significantly) larger than that of girls.” Moreover, the intraindividual strength of reading in girls was larger than in boys and the reverse was true for mathematics. They add, “the sex differences in intraindividual academic strengths were near universal.” Furthermore, the strengths in their respective domains were exacerbated in the more gender-equal countries, i.e., countries with a higher rating on the Global Gender Gap Index showed a bigger inequality in performance.

The Global Gender Gap Index (GGGI) attempts to address the degree to which girls and women fall behind boys and men on 14 variables – e.g., earnings, tertiary enrollment ratio, life expectancy, seats in parliament, et cetera – on a 0.0 to 1.0 scale, with 1.0 representing complete parity or men falling behind. The worst on the list was United Arab Emirates (UAE) at .593 and the best was Iceland at .881.


Equality = bad for women

Graph ‘a’ is comparing the gap between sexes in science efficacy and how equal the country rates in the GGGI. We can see from the line of best fit that the higher a country is on the GGGI, the higher its gap in intraindividual science performance. The inverse is true for graph ‘b’, comparing the same GGGI to the percentage of women as STEM graduates. When laid out for us, the evidence is stark. The more equal a country is, the more its citizens are free to do what they wish, which makes way for their temperamental predispositions.

The strength percentages for girls was 24 percent in science, 25 percent in mathematics, and 51 percent in reading. For boys, it was 42 percent in mathematics, 38 percent in science, and 20 percent in reading. It’s time we discuss the conditioning boys face from a young age about how bad they are at reading.

Polling the kids about their attitudes toward science, they found that boys rated their self-efficacy higher than the girls did in 39 of 67 (58%) countries, and the gap was biggest in the gender-equal countries. Boys also rated their interest in science higher than the girls in 51 (76%) countries. Lastly, the boys rated their enjoyment of science greater than girls’ in 29 (43%) countries, with a bigger gap in the gender-equal countries.

gggi science.jpeg

A fascinating implication of the data is that the higher-scoring countries in the GGGI had higher percentages of girls who showed their strength in math or science. This means that the difference between the percentage of girls who showed strength in math or science and those same women who went on to graduate from a STEM field became larger in more gender-equal countries.

The more freedom women have, the more they will stay away from STEM.

Testing, Testing

I, a saint, compiled the SAT scores of all college-bound seniors for critical reading, writing, and mathematics since 1972 and split it by sex, of course. I included a picture of the data for the Skeptical Reader.

Screen Shot 2019-01-09 at 9.58.39 AM.png

SAT scores of college-bound seniors.

At first glance, the differences seem to be negligible, although the math differential is much larger than in reading and writing. I ran quick t-tests just to show how confident we can be that the means truly are different, although it’s obvious because the sample sizes are so large (standard deviations seemed to always land between 105 and 125, so I used 115 as our number and estimated about 500,000 seniors per year, on average, took the SAT, although it’s been well over 1,000,000 for several consecutive years).

For a potentially meaningful insight, I ran the means through a Cohen’s d calculator to check the effect sizes, i.e., the significance of the difference. As with first glance, the reading and writing effect sizes are tiny, totally unremarkable. The mathematics effect size is a bit more significant.

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Reading – group 1 = boys, group 2 = girls

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Mathematics – group 1 = boys, group 2 = girls

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Writing – group 1 = boys, group 2 = girls

Because the Reader is a Learned Scholar, he knows that an effect size of 0.20 is considered “small,” 0.50 is considered “moderate,” and 0.80 is considered to be “large,” with 1.0 being equivalent to one full standard deviation difference. There is some nasally arguing about this index, but Cohen’s guidelines are essentially THE guidelines, especially for the social sciences.

However, I could be a total stickler and provide this study, wherein 380 meta-analyses are combined to look at what science has provided in relation to effect sizes. Compared to the data, we see that Cohen’s numbers are almost the Platonic Ideal of what we wish effect sizes would provide, but way above what the Real World has provided over several decades of research.

screen shot 2019-01-10 at 3.07.46 pm

What we actually find is that anything over ~0.30 is in the upper third of effect sizes for a massive chunk of studies. While this doesn’t remotely mean that we need to scrap Cohen’s guidelines, it hints that the context of an effect size may be just as important as the arbitrary calculation itself.

Anyway, I don’t believe the differences in mean SAT scores are that important, even if it’s possible for me to point at 0.32 as a Serious Number.

For the sake of my mental health, I’d rather not compile the statistics for the ACT, as it seems the results from the years I did research are pretty much identical – girls ahead slightly in reading and writing with about a third of a standard deviation between them in mathematics and science.

Between the PISA and SAT, we can see that women and men are not identical in their reading, writing, math, and science performance. However, the gaps aren’t as massive as we’d expect from the discrepancy in STEM degrees, so there should be a lot more overlap, right? Well, enough with all of this empiricism and inference gobbledygook. Let us discuss what really matters, do girls just wanna have fun?

Things or People: The Choice Is You’res

A meta-analysis from 2009 ran over 47 inventories, published between 1964 and 2007, and a total of 81 samples consisting of 243,670 men and 259,518 women. The purpose of the study was to understand the preferences of women and men.

In the intro, the meta-analyzers cite a 1987 hypothesis from Betz and Fitzgerald explaining that women generally tend to display more interest in social and artistic activities, and men indicate interest in science, technical, and mechanical activities. They were merely echoing Edward Thorndike’s 1911 proclamation that the greatest difference in men and women is “in the relative strength of the interest in things and their mechanisms (stronger in men) and the interest in persons and their feelings (stronger in women).”

Can Data Corroborate the Claims of the Sexists?

The meta-analyzers used a Things-People and a Data-Ideas dimension to see where men and women stack up against each other in general interest. The other tool was RIASEC, a theoretical framework for vocational interest created by John Holland. It allows us to get a structured idea of what one’s mind is geared toward. RIASEC: Realistic interest in working with things and gadgets or working outdoors; Investigative interest in science, including mathematics, physical and social sciences, and biological and medical sciences; Artistic interest in creative expression, including writing and the visual and performing arts; Social interest in helping people; Enterprising interest in working in leadership or persuasive roles directed toward achieving economic objectives; and Conventional interest in working in well-structured environments, especially business settings.

screen shot 2019-01-10 at 8.57.23 am

Effect sizes plotted on Things-People and Data-Ideas dimensions.

Men and women differed by almost an entire standard deviation (d = 0.93) when studied in the Things-People dimension. If the Reader forgot, scroll back up to the chart showing how huge an effect size of 0.93 really is. It indicates that only 46.9 percent of the distributions of interest on the Things-People dimension for males and females showed overlap, meaning upwards of 82.4 percent of males have stronger inclinations toward things-oriented vocations than the average female.

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Men have stronger Realistic, Investigative, and STEM interests, while women show more fascination with Social and Artistic fields. This parallels the findings on the People-Things index quite closely.

The mean effect size of .84 on the Realistic index and 1.11 for engineering interests suggest extremely large differences in vocational interest. This means only 13.4 percent of women showed more interest in engineering than the average man and 74.9 percent of women showed stronger Social interest than the average man.

Recall the data from Mind the Gap in which we saw that women make up only 27 percent of engineering, manufacturing, and construction jobs. If only 13.4 percent of women showed more interest in engineering than the average man, then we could actually say women seem to be overrepresented in these STEM fields. However, I would never put forth such hateful bigotry as a legitimate claim.

The PISA data, SAT scores, and the effect sizes for the Investigative scale (d = 0.26), as well as for science (d = 0.36) and mathematics (d = 0.34) basic interest scales from the last meta-analysis, were within Cohen’s “small” ranges. This shows that, while on paper men and women don’t seem much different, the minor differentiations they do show have large consequences when choosing vocations. Small statistical discrepancies seem to follow a butterfly effect in the Real World.

Even the folks who look at these data with an a priori conviction that male-female STEM inconsistencies are bad cannot ignore how obvious it is that men and women simply want different careers.

Radicalize Me, Captain!

  1. The more gender-equal a country is, the more likely women are to avoid STEM fields as an enjoyable career path.
  2. On average, men enjoy working with things more than people and the inverse is true for women.

Once these two facts are thoroughly mulled and understood, it becomes quite easy to accept sex differences in certain fields. Moreover, I believe it would be bizarre if we didn’t see these dissimilarities in the Real World. Only 13.4 percent of women show more interest in engineering than the average man, yet we have a 50-50 split of men and women in the field, what the hell?

Obviously, then, there is nothing corrupt or “wrong” with computer science. It’s possible that not many women go into computer science because they don’t want to go into computer science. Moreover, it’s a lot more likely than a nebulous claim of systemic oppression and hegemonic masculinity (my whole life I’ve listened to teachers and authorities tell girls that they can do everything boys can do, and better. The Anything You Can Do propaganda started in 1950, almost 70 years ago.).

Computer science is a brand new field, one that was in its infancy when the third wave of feminism was putting on its first pantsuit. If anything, computer science is the first Real World test given in places where men and women have all the same rights and it’s illegal to discriminate against someone for their ascribed status, i.e., a characteristic with which they were born.

Perhaps men and women are just different.

Computer: Zoom Out

Americans, and probably the whole of “the West,” talk constantly about college and credentials. How many times has the Reader heard somebody say “97 percent of climate scientists agree” or, worse, how many times has the Reader used that statistic? It is the definition of argumentum ad verecundiam, an appeal to authority. Not only is this climate example incredibly stupid, but the whole idea of credential fetishizing is shortsighted.

We should not care much about one’s credentials unless we are hiring them for a job, although most people who hire talent will admit experience is far more attractive than degrees. It’s very extremely really important to remember that all of this “credentializing” and focus on degrees is barely two generations old. The universities weren’t flooded until after the 60s and the vast majority of the world was totally illiterate before the 1900s.



Since men and women just recently began running toward college (a whole other can of worms), the realization that men and women have different predispositions of interest in education is also brand new. Why are we assuming this is a problem that needs to be fixed rather than an empirical display of the difference between male-female temperaments that have been ingrained over 200,000 years of evolution?

Recall my mind-bending thought experiment at the beginning of this article. Is it more likely that the Red team likes basketball more and is better at it, or is it more likely that a system of oppression and a tyrannical rule of law is keeping the Blue team from excelling at exactly the same rate as the Red team? Should every game end in a tie before we let them play without third-party intervention?

The final meta-analysis above included the following sentence in their discussion, “despite improvement over the past four decades in the number of women pursuing careers in the STEM fields, the continued underrepresentation of women in these fields is an issue of great concern to researchers and policy makers.”

Why? I can understand why it would be intriguing to researchers, e.g., credentialism is brand new, much of the focuses in STEM fields are brand new, colleges are flooded with students in the 21st century, women in less-equal areas achieve more in STEM, et cetera, but why does it concern policymakers? Their job is to stay out of our way and protect our rights. What do they care if women don’t like STEM? Why is that a concern but the underrepresentation of men in non-STEM jobs, a much bigger pool, is not a concern? How about the underrepresentation of women in these jobs?


I could not care less about the underrepresentation of men or women in any fields, but I have a problem with people ignoring the obvious for ideological purposes. I especially object to the idea of any kind of affirmative action to force men out of STEM in order to fill their chairs with women. If, or perhaps when that happens, we should burn down the universities.

“Well, that about does her, wraps her all up… I guess that’s the way the whole darned human comedy keeps perpetuatin’ itself, down through the generations, westward the wagons, across the sands of time until we– aw, look at me, I’m ramblin’ again. Well, I hope you folks enjoyed yourselves. Catch ya later on down the trail.” – The Stranger

One comment

  1. People that keep pointing out that there is gender inequality in STEM caused by bias and stereotypes seldom give evidence that demonstrates this and few ever engage with studies that show differences in vocational interests and differences in abilities.


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