1. The selection criteria are the same for different populations, and the there are significant statistical differences in the populations that may lead to the observed result.
2. The selection criteria are the same for different populations, and random noise produced the observed result.
3. The selection criteria are different for different populations.
You cannot assert one without ruling out the others.
In the fighter pilot case, I imagine that it would be easy to disprove #1 and #2. Not all cases of discrimination are so obvious that there are no non-imaginary solutions to the equations that conform to the assumptions of the hypothesis.
It makes some people uncomfortable to face the idea that in order to prove discrimination is occurring, they must first employ accurate statistics for partitioned populations, which may be unflattering to one or more of the partitioned groups. You can't really say "because race" or "because sex" or "because disability" with certainty over "because selection-relevant attribute" using a statistical argument without showing the statistics. But those who attempt to gather accurate statistics are often accused of something-ism and vilified just because some people don't like the results.
Some people just want to work without constantly watching their backs and covering their asses. And that's why we assume independence without having ironclad proof that is also easily understood by the general public. If you're going to suggest that there is something intrinsic to femaleness that causally links to less engineering skill, you had better have a flawless data set with mathematically perfect analysis, and then also invent a psychological "out" which absolves anyone of guilt for relying on the results.
This is why all HR departments suck. Most of their job is covering someone else's ass, and if they choose to use statistics, they must be both factually correct and politically correct.
1. The selection criteria are the same for different populations, and the there are significant statistical differences in the populations that may lead to the observed result.
2. The selection criteria are the same for different populations, and random noise produced the observed result.
3. The selection criteria are different for different populations.
You cannot assert one without ruling out the others.
In the fighter pilot case, I imagine that it would be easy to disprove #1 and #2. Not all cases of discrimination are so obvious that there are no non-imaginary solutions to the equations that conform to the assumptions of the hypothesis.
It makes some people uncomfortable to face the idea that in order to prove discrimination is occurring, they must first employ accurate statistics for partitioned populations, which may be unflattering to one or more of the partitioned groups. You can't really say "because race" or "because sex" or "because disability" with certainty over "because selection-relevant attribute" using a statistical argument without showing the statistics. But those who attempt to gather accurate statistics are often accused of something-ism and vilified just because some people don't like the results.
Some people just want to work without constantly watching their backs and covering their asses. And that's why we assume independence without having ironclad proof that is also easily understood by the general public. If you're going to suggest that there is something intrinsic to femaleness that causally links to less engineering skill, you had better have a flawless data set with mathematically perfect analysis, and then also invent a psychological "out" which absolves anyone of guilt for relying on the results.
This is why all HR departments suck. Most of their job is covering someone else's ass, and if they choose to use statistics, they must be both factually correct and politically correct.