[godelski]

Anyone else find it weird that when you click "other" for gender that the data looks more like garbage?

I was trying to actually compare male and female salaries out of interest but have a hard time believing so many people earn <$20k/yr. Even when you switch the filters around. The best I could find is just filtering for the US, but the number of respondents are so low, ~1k total (~200 Females, ~800 males), that it becomes difficult to make accurate comparisons ($22k diff but women had more masters degrees and similar PhDs, by percentage).

Has anyone sorted through this data and tried to account for these factors? I'd be interested at the uncertainty and how the information was gathered.

[jerednel]

I pulled a few gender stats here. http://bit.ly/2zjrSJD Accounting for country, education, and industry you really reduce the population you're sampling from but those deviations are huge. You need to account for industry especially.

[godelski]

Well this really doesn't discuss the error associated with the data. Which is what I was trying to get at. There seems to be a lot associated with it, which makes accurate predictions difficult to make.