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21-Jan-2020 01:45 by 6 Comments

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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.

Then being non-gendered would be a higher sign of quality in a man than in a woman.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.In other words, if you know somebody’s a woman, you’re more likely to approve her request than you would be on the merits alone.We can’t quantify exactly how much this is, because the paper doesn’t provide numbers, just graphs.This alone can’t rule out that one gender is genuinely doing something differently than another, so they had another neat trick: they wrote another program that automatically scored accounts on obvious gender cues: for example, somebody whose nickname was Jane Smith01, or somebody who had a photo of themselves on their profile.

By comparing obviously gendered participants with non-obviously gendered participants whom the researchers had nevertheless been able to find the gender of, they should be able to tell whether there’s gender bias in request acceptances.

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.

There is a similar drop for men, but the effect is not as strong.” In other words, they conclude there is gender bias among outsiders because obvious-women do worse than gender-anonymized-women.

They admit that obvious-men also do worse than gender-anonymized men, but they ignore this effect because it’s smaller.

Eyeballing the graph, it looks like being a woman gives you about a 1% advantage.

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