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Eyeballing the graph, it looks like being a woman gives you about a 1% advantage.
women and whether one gender gets a higher acceptance rate than the other.
This is a little harder than it sounds – people on Git Hub use nicks that don’t always give gender cues – but the researchers wrote a program to automatically link contributor emails to Google Plus pages so they could figure out users’ genders.
The study does not provide enough information to determine whether this is statistically significant.
Eyeballing it it looks like it might be, just barely. The study describes its main finding as being that women have fewer requests approved when their gender is known.
) but they do give us a subgroup analysis by “insider status”, ie whether the person has contributed to that project before.
Among insiders, women do the same as men when gender is hidden, but better than men when gender is revealed.This is obviously a silly just-so story, but my point is that without knowing why all genders show a decline after unblinding, it’s premature to speculate about why their declines are of different magnitudes – and it doesn’t take much to get so small a difference. There’s no study-wide analysis, and no description of how many different subgroup analyses the study tried before settling on Insiders vs.Outsiders (nor how many different definitions of Insider vs. Remember, for every subgroup you try, you need to do a Bonferroni correction.Because Git Hub is big and their study is automated, they manage to get a really nice sample size – about 2.5 million pull requests by men and 150,000 by women. ) requests accepted than men for all of the top ten programming languages.They check some possible confounders – whether women make smaller changes (easier to get accepted) or whether their changes are more likely to serve an immediate project need (again, easier to get accepted) and in fact find the opposite – women’s changes are larger and less likely to serve project needs.That makes their better performance extra impressive.