One sunny day we arrive at work in the university administration to find a lot of aggressive emails in our in‒box. Just the day before, a news story about gender discrimination in academia was published in a popular local newspaper which included data from our university. The emails are a result of that. Female readers are outraged that men were accepted at the university at a higher rate, while male readers are angry that women were favored in each course the university offers. Somewhat puzzled, you take a look at the data to see what’s going on and who’s wrong.
The university only offers two courses: physics and sociology. In total, 1000 men and 1000 women applied. Here’s the breakdown:
800 men applied ‒ 480 accepted (60 %)
100 women applied ‒ 80 accepted (80 %)
200 men applied ‒ 40 accepted (20 %)
900 women applied ‒ 360 accepted (40 %)
Seems like the male readers are right. In each course women were favored. But why the outrage by female readers? Maybe they focused more on the following piece of data. Let’s count how many men and women were accepted overall.
1000 men applied ‒ 520 accepted (52 %)
1000 women applied ‒ 440 accepted (44 %)
Wait, what? How did that happen? Suddenly the situation seems reversed. What looked like a clear case of discrimination of male students turned into a case of discrimination of female students by simple addition. How can that be explained?
The paradoxical situation is caused by the different capacities of the two departments as well as the student’s overall preferences. While the physics department, the top choice of male students, could accept 560 students, the smaller sociology department, the top choice of female students, could only take on 400 students. So a higher acceptance rate of male students is to be expected even if women are slightly favored in each course.
While this might seem to you like an overly artificial example to demonstrate an obscure statistical phenomenon, I’m sure the University of California (Berkeley) would beg to differ. It was sued in 1973 for bias against women on the basis of these admission rates:
8442 men applied ‒ 3715 accepted (44 %)
4321 women applied ‒ 1512 accepted (35 %)
A further analysis of the data however showed that women were favored in almost all departments ‒ Simpson’s paradox at work. The paradox also appeared (and keeps on appearing) in clinical trials. A certain treatment might be favored in individual groups, but still prove to be inferior in the aggregate data.