>You can use this technique whenever (a) you have at least a random sample of the applicants that were selected, (b) their subsequent performance is measured, and (c) the groups of applicants you're comparing have roughly equal distribution of ability.
So yes, OP is ignoring the entire premise of PG's argument.
OP acknowledges this... "Unfortunately, using the mean as a test statistic is flawed - it only works when the pre-selection distribution of A and B is identical, at least beyond C."
To me the rest of the article is asks the question, "requirement (c) is really strong, is there a way we can use post-selection statistics to determine bias while weakening (c)? what if we tried measuring the post-selection minimum instead of the mean?"
Also PG edited his essay to add that disclaimer only after WildUtah's comment, so it's possible that OP hasn't read the updated version.
>You can use this technique whenever (a) you have at least a random sample of the applicants that were selected, (b) their subsequent performance is measured, and (c) the groups of applicants you're comparing have roughly equal distribution of ability.
So yes, OP is ignoring the entire premise of PG's argument.