How a statistical paradox helps to get to the root of bias in college admissions

Simpson’s paradox is a statistical phenomenon in which a trend appears in small data sets, but differs or reverses when those sets are combined into a larger group. One of the most fascinating examples of the paradox comes from a study about gender bias in graduate admissions at the University of California, Berkeley in 1973, when roughly 44 per cent of male applicants were accepted, compared with only 35 per cent of female applicants. These figures appeared to show an obvious bias against women, but when the data were broken down by department, they actually showed a slight bias in favour of women. This animation from MinutePhysics explains just how Simpson’s paradox occurs and, in the case of Berkeley, how the paradox highlighted a deeper societal bias that pushes women towards departments that are more crowded, have less funding, and offer poorer employment opportunities.