I would suspect the larger issue is that people are probably much worse at identifying what performance metrics for selection convert to their respective performance for success.
I would suspect the larger issue is that people are probably much worse at identifying what performance metrics for selection convert to their respective performance for success.
The effect is the same isn't it? Less chance for a student with ancestors from a particular geographic locale getting a placement.
The "high performance means there's a bias" theory only works if you assume that everyone is starting from the same social, cultural, and physical baseline. They aren't.
Maybe a better metric would be if there were no mediocre data points among a certain group; that would be more evidence (but still not necessarily good evidence) that you have to exceptional to get attention and overcome the "bias barrier", not simply that most of a certain type of performer does better than a different type.
In [14]: x = norm(0.0,1).rvs(100000)
In [15]: mean(x[where(x > 2.0)])
Out[15]: 2.3774795090391301
In [16]: y = norm(0.5,1).rvs(100000)
In [17]: mean(y[where(y > 2.0)])
Out[17]: 2.4372124830289557
I.e., a difference in the mean of 0.5 sigma corresponds to 0.06 in Graham's test statistic.Graham is a little bit off - a better place to look for bias is the bottom of the accepted distribution than at the mean.