Encore un outil d'ajustement de modèles bayésien, centrésur les processus gaussiens...

There’s been a movement which has said that most research is wrong. It’s making people feel they’re doing something wrong, but that’s not the problem. The problem is that the publication system pushes you because you can only publish if you get a good, that is, small, p-value [a statistical test that indicates whether results could be due to chance]. Researchers then massage the data until they get the p-value and then it’s not reproducible. But if we were much more transparent and said, “You’re allowed to publish things which are significant or not significant because it’s useful down the road and just publish all your data and the code you used for the analyses” – if you’re transparent about what you’re doing, there’s much less opportunity to shoehorn the data into some wrong conclusion.

I feel that people misuse summaries in statistics. They feel as if statistics is going to summarize everything into one value, as if one p-value is going to summarize five years of work. It’s ridiculous. Everything is multidimensional, it’s complex. But if we could publish more of the negative results and all of the data, we would advance science much faster, because people would get insight from the negative results.

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