Recent comments in /f/dataisbeautiful

coffeesharkpie t1_j5e1g0z wrote

Well, you know it's a common notion in statistics that "All models are wrong, but some are useful". This means no model will ever capture reality as is, but we can make sure the model is good enough to be useful for the particular application. This is possible because we can actually quantify uncertainty about prior information, estimates and predictions (e.g. through credible or confidence intervals) and make sure models are as exact and as complex as needed.

Funnily, we can predict things quite well, especially when it comes to large numbers of people (individuals are the hard stuff). Like how social background influences educational levels for a population, how lifestyle will influence average health, how climate change may affect frequency of extreme weather, even what people may want to write on their smartphones is predicted with these kind of models.

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Terminarch t1_j5dpftk wrote

Reporting for duty!

Anyway, this reminds me of a study I reviewed recently. Literally it estimated lives saved from number of DEATHS, vaccination rate, and assumed vaccine effectiveness. When double the people died it gave credit for double lives saved. Fucking brilliant.

And if I'm remembering correctly it was cited on Google Scholar over 60 times in a year. What does that tell you about the quality of "science"?

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nutsbonkers t1_j5dk9q1 wrote

We can predict the future, with a degree of confidence. The statistical models used in this un peer reviewed paper have been peer reviewed. The math they used is sound because it's been peer reviewed and deemed appropriate and accurate enough. I'm sure it will be reviewed in the future, Vice or whoever just wants to get a jump on a good article.

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