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currentscurrents t1_j7y4073 wrote

Reply to comment by [deleted] in [D] Critique of statistics research from machine learning perspectives (and vice versa)? by fromnighttilldawn

>Right now basically all progress is with large models,

You mean all progress... in machine learning. A lot of scientific fields necessarily must make do with a smaller number of data points.

You can't test a new drug on a million people, especially in early phase trials. Even outside of medicine, you may have very few samples if you're studying a rare phenomena.

Statistics gives you tools to make limited conclusions from small samples, and also measure how meaningful those conclusions actually are.

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[deleted] t1_j7y67bi wrote

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[deleted] t1_j7y9mjs wrote

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WikiSummarizerBot t1_j7y9nn5 wrote

All models are wrong

>All models are wrong is a common aphorism in statistics; it is often expanded as "All models are wrong, but some are useful". The aphorism acknowledges that statistical models always fall short of the complexities of reality but can still be useful nonetheless. The aphorism originally referred just to statistical models, but it is now sometimes used for scientific models in general. The aphorism is generally attributed to the statistician George Box.

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