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subtypefiddler | 2 years ago

She wrote a paper on blind auditions, this gained a lot of media attention, but as a lot of her work it was quite exaggerated see e.g [0]

[0] https://statmodeling.stat.columbia.edu/2019/05/11/did-blind-...

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ace32229|2 years ago

Perhaps worthwhile to point out in this context, her work was exaggerated by others, not by herself.

tnecniv|2 years ago

This is incredibly common due to multiple levels of media hype. I got interviewed by someone from university PR for a series on what different grad students were working on during my PhD. We had a 30 minute phone call and the result a week later was an article that I was allowed to edit before release. The article was conveyed the most ambitious possible take of my research agenda and had a rather forced narrative relating it to the department beer league softball team I played on for a single season at that point. I could have rewrote the whole thing but it wasn’t factually wrong and I had neither the time nor care to do so.

This didn’t happen to me, but these university press releases then get picked up by science journalists who, maybe with an additional interview, build off it for their own article and hype up the work and narrative even more. It’s like a game of hype telephone.

LudwigNagasena|2 years ago

The quotes from her article suggest otherwise.

Claudia Goldin and Cecilia Rouse:

> Although some of our estimates have large standard errors and there is one persistent effect in the opposite direction, the weight of the evidence suggests that the blind audition procedure fostered impartiality in hiring and increased the proportion women in symphony orchestras.

> …

> The weight of the evidence, however, is what we find most persuasive and what we have emphasized. The point estimates, moreover, are almost all economically significant.

Andrew Gelman:

> This is not very impressive at all. Some fine words but the punchline seems to be that the data are too noisy to form any strong conclusions. And the bit about the point estimates being “economically significant”—that doesn’t mean anything at all. That’s just what you get when you have a small sample and noisy data, you get noisy estimates so you can get big numbers.