Emily Oster has founded her entire career on weak data, data mining and faulty scholarship. Her modus operandi is making very strong claims that conform to the priors of her target audience and "support" them with weak data and shoddy analysis. She is a master at marketing herself and finding powerful protectors in the empirical economics complex, though. The fact that her latest piece is weak should surprise nobody.
I looked in detail at her paper for the Massachusetts schools having no difference in Covid cases for 3ft vs. 6ft desk spacing. The data was not of high quality, and there was no effort to analyze if the data was consistent. And the data had lots of other fields (masks worn or not, etc.), but somehow, there was always a missing piece so that the only conclusion could be about desk spacing. It was like reverse p-hacking: collect raw data, look at it to see what can be gleaned, and then make that your hypothesis.
But how confident can we be that going virtual again has a higher NPV than keeping schools open with a test to stay/return protocol? I think school closures had massive costs in terms of instruction value lost and parental time for doubtful benefits in reduction of spread
"Data-driven does not mean error-free" - AMEN! Thanks for laying out the case for a healthy dose of skepticism here.
Thank you Claudia, the world is fortunate to have you on our side.
Emily Oster has founded her entire career on weak data, data mining and faulty scholarship. Her modus operandi is making very strong claims that conform to the priors of her target audience and "support" them with weak data and shoddy analysis. She is a master at marketing herself and finding powerful protectors in the empirical economics complex, though. The fact that her latest piece is weak should surprise nobody.
I looked in detail at her paper for the Massachusetts schools having no difference in Covid cases for 3ft vs. 6ft desk spacing. The data was not of high quality, and there was no effort to analyze if the data was consistent. And the data had lots of other fields (masks worn or not, etc.), but somehow, there was always a missing piece so that the only conclusion could be about desk spacing. It was like reverse p-hacking: collect raw data, look at it to see what can be gleaned, and then make that your hypothesis.
But how confident can we be that going virtual again has a higher NPV than keeping schools open with a test to stay/return protocol? I think school closures had massive costs in terms of instruction value lost and parental time for doubtful benefits in reduction of spread