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Comment: Detailed look at review process/Sample size (Score 1) 464

by urbazewski (#37146820) Attached to: American Grant Writing: Race Matters

I am mystified by number of commenters saying that you can't draw conclusions because of the small sample size. This is not a small sample size. The results are robust in part because the data set is so large: 83000 observations.

One of the interesting points of TFA is that it pinpoints where in NIH's two-tier process of review black researchers are eliminated. They are more likely to be eliminated in the first round of peer review than in the second "scoring" round. This is not consistent with them submitting, on average, less worthy projects. If that were the case, they should have the same or a higher attrition rate at the second level of review. Black researchers are also less likely to resubmit grants, which could be a simple lack of mentoring or communication from the NIH.

From TFA:

We find it troubling that the typical measures of scientific achievement—NIH training, previous grants, publications, and citations—do not translate to the same level of application success across race and ethnic groups. Our models controlled for demographics, education and training, employer characteristics, NIH experience, and research productivity, yet they did not explain why blacks are 10 percentage points less likely to receive R01 funding compared with whites.

Comment: Re:Statistics (Score 1) 464

by urbazewski (#37146282) Attached to: American Grant Writing: Race Matters
This is based on a data set with 83000 grant proposals from about 40000 researchers, every application made to the largest NIH program over a 5 year period. This is not a small sample and it is not a single point in time. If you flipped a coin 83000 times and got heads 49,800 times I'd say the coin is biased in favor of heads, oh, by about 60%.

Everyone can be taught to sculpt: Michelangelo would have had to be taught how not to. So it is with the great programmers.

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