>The magnitude of Systemic Antiracism in medical school admissions: A black applicant with a 3.2–3.39 GPA and a 24–26 MCAT had almost a ten times greater chance of admission than an Asian-American with the same scores.

I don't know about studies on actual patient outcomes, but there are good data WRT admissions, which I think is relevant to OP's point about overcoming obstacles.

The story is exactly the opposite of the one that you're telling!

We first found out that GPA and GRE scores mean nothing. Neither GPA nor GRE scores are predictive of grad school performance!

Same with LSAT. It has no predictive validity for your quality as a lawyer. Just about 0.5% of your abilities as a lawyer are related to your LAST score. (not 50%, less than 1%) https://www.alanet.org/legal-management/2023/february/column...

MCAT scores are a bit more predictive, but only in the stupidest way. Very low MCAT scores are very predictive that you will be terrible and fail out. MCAT is only predictive at the bottom, above the lowest 25% of scores, all MCAT scores are the same.

We don't have a good way of scoring people to get them into these positions. So instead of blindly following a score that we know is meaningless, we try to have some equity of opportunity in society. And, we try to make sure that people have doctors, scientists, etc. that they feel comfortable with and that represent their communities too.

And anyway, since when does someone have to be the same race as you to be part of the same community?

And how many Asians do you think are in that quartile? So they were right, given that choice go with the Asian, thanks to affirmative action we know they are less likely to be in the bottom and therefore less likely to be terrible.

This is a very poor source - an article from a company with no explanation of their method of determining "success" for lawyers.

I’m skeptical of these claims because I’ve heard similar things about the SATs, but: https://news.mit.edu/2022/stuart-schmill-sat-act-requirement...

When you select a population by a criteria the selection itself eliminates the correlation.

So for example if you only accept high LSAT scores to be lawyers you should expect to find LSAT scores to be largely uncorrelated-- the correlation has already been removed. In fact, you may find it be be _inversely_ correlated because the parties with weaker scores that still made it through had other things going for them that made them successful. And this is true even if the test performance is HIGHLY predictive considering the whole population.

And this is clearly true: if nothing else LSAT tests the ability to understand written English and the kinds of basic logical reasoning that are absolutely required in the law. There are some people who do poorly at those tasks and would score very poorly at the LSAT, and would be huge liabilities if they became lawyers.

Now, perhaps those low performers would be filtered out by other criteria ultimately (say the bar exam) and so the LSAT may be redundant in that sense (except for saving huge costs and time for people who would ultimately flunk out...).

All that said, it's common for tests to get overweighed because they're the number we have. Would you prefer the lawyer that has the 5pt higher LSAT score but never worked a case like your vs one who successfully handles them all the time? Obviously the latter! but many other predictors are often not available and ones that are available are often not reducible to numbers or are situation specific.

In any case reasoning from post selected statistics has produced some disastrous decisions in business. Sadly, most people are not in the position to conduct a controlled study most of the time and so the stats you get are always tainted by post-selection effects.

I know someone whose work conducted basic coding tests on applicants. They found that scores on the test didn't predict performance (or were even somewhat anti-predictive: the people who nailed the tests were sometimes fakers who had managed to study for the test). So they eliminated it and then suffered disaster after disaster. The newer hires were generally not as good at their jobs, entirely contrary to the expectations from their prior results.