Some good points from the article:
- “'Statistics sounds like this dry, technical subject, but it draws on deep philosophical debates about the nature of reality,' said the Princeton University astrophysicist Edwin Turner, who has witnessed a widespread conversion to Bayesian thinking in his field over the last 15 years."
- "But there’s a danger in this tradition, said Andrew Gelman, a statistics professor at Columbia. Even if scientists always did the calculations correctly — and they don’t, he argues — accepting everything with a p-value of 5 percent means that one in 20 “statistically significant” results are nothing but random noise. The proportion of wrong results published in prominent journals is probably even higher, he said, because such findings are often surprising and appealingly counterintuitive, said Dr. Gelman, an occasional contributor to Science Times...He suggests using Bayesian calculations not necessarily to replace classical statistics but to flag spurious results."
- "Others say that in confronting the so-called replication crisis, the best cure for misleading findings is not Bayesian statistics, but good frequentist ones. It was frequentist statistics that allowed people to uncover all the problems with irreproducible research in the first place, said Deborah Mayo, a philosopher of science at Virginia Tech. The technique was developed to distinguish real effects from chance, and to prevent scientists from fooling themselves...Bayesian statistics, in short, can’t save us from bad science."
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