Actual Linked study in Article Disagrees.
May 03, 2017
6:56 AM EDT
Study Discussion wrote:Discussion Why do differences exist in acceptance rates?
Inconclusive result. Female and male coding is accepted at nearly the same rate, per study. People see problems when they wish to see problems...
Please note that the last paragraph of the quote from the study clearly shows both a bias in the study toward supporting an otherwise unsupported conclusion as well as a problem in the rigor of the analysis.
The statement... "Women tend to be less risk adverse than men." Is a statement applied to a statistical sample of women vs. men.
The statement... "Women tend to have large code contributions." Is a statement applied to a particular women contributor.
Therefore the last paragraph is inconsistent. They are comparing individual with a group... Apples and Oranges and that sort of thing.
Therefore... there is no problem to discuss.
May 03, 2017
11:35 AM EDT
|yes, the study concludes that women got accepted more often, but the point of the article is that in a number of cases it was not known that they were women because one could not identify their gender by their name.
however i am still wondering where the story is, because the article quotes that 58% of identifiable women got accepted vs 61% of identifiable men, whereas everything else was gender neutral.
a difference of 3 percent points? is that even significant?
i mean, i am all for finding evidence that more work needs to be done to achieve equality between men and women. there is plenty of anecdotal evidence, like here for example: https://twitter.com/SchneidRemarks/status/839910253680553988
but if we want to show evidence, it needs to be real, and not just pulled out of a hat.
May 04, 2017
12:31 PM EDT
|Note that "significant" <> "universal applicability". Another problem is the disparity in size between both groups. A difference of 15 fold is not something you step over that easily. Furthermore, they've taken only one single attribute, which is gender. How diverse other attributes (e.g. age, education, race) are distributed is.. unknown. To give you an example, if you would apply the same method along people working in theoretical physics, you would get that people with ALS (Lou Gehring disease) are much more successful. The lack of any real conclusion doesn't make the paper much stronger anyway. Yes, there is a 3% difference. Now what. Blame it on all these hard working sexist developers, like some former editors of LXer used to do.|
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