Before-the-fact EV is what the entire question of bias is about. In the case of investments, your minimum outcome is that you shut down the company. Comparing

Before-the-fact EV is what the entire question of bias is about. In the case of investments, your minimum outcome is that you shut down the company. Comparing this for male/female sets is useless. Also, looking at other aspects of the distribution (e.g. how many $10M exits, $100M exits) isn't useful because for investing, you want to maximize mean return, and it's not like looking at 20-80%tile outcomes numerically tells you much about that -- are these people saving dead companies or are they derisking $10B companies? With students there is more of a connection between before-the-fact EV and outcomes, and much more information exposed (grades and course registrations on a semester-by-semester basis). Without that information (if you just looked at who graduated, who didn't) you can't really say as much. (You could run a Netflix-like competition to see who has the best graduation prediction engine and use its estimates as your definitive answer to what an unbiased EV predictor would say about college applicants.) With GPA information, let's say you look at the 10th percentile GPA's in each set and below, for boys it's 0.1-1.1 and for girls it's 0.1-1.5. What does that tell you about before-the-fact EV cutoffs? It doesn't tell you much, because one set of students, when they fail, could be more likely to fail harder than the other. It takes a lot more work than just that.