[roenxi]

I'm not quite sure I follow your complaint, but I think I might be disagreeing with you. A key lesson of Simpson's Paradox is you can't read stories into data without having a causal model derived from outside the data.

I can comfortably invent stories that are not inconsistent with the data for a wide range of scenarios:

1) Only the most capable women are applying to Dept A due to discrimination, so the data is evidence of discrimination.

2) Dept A is discriminating towards women (self evident, 80% vs 60% admissions).

3) Dept A is completely non-discriminatory and the assessors are unaware of the gender of applicants; the differences are due to personal choices w.r.t. education and social networks turning out to be proxies for gender.

No study this sort of data can detect gender bias. It can be used as evidence in a broader study that comes up with a causal model for how the admissions process works; but there is no getting around interviews and field observations.

[YeGoblynQueenne]

I'm not challenging Simpson's paradox, only the conclusion quoted in respect with the data in the above table (I'm still not sure where it came from).