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eg's avatar

I would be interested to see an experiment of the following form:

Pick a bunch of things from the pot of crud correlations.

Give them to theoreticians. Tell the theoreticians whatever things they got are strongly correlates (even if they are actually inversely correlated).

Have the theoreticians come up with an explanation for whatever things they were given.

Have other people rate the explanations the theoreticians come up with.

Are the explanations for things with actually positive correlations rated as being better than the explanations for things with secretly negative correlations?

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Tom Dietterich's avatar

All good points: This situation calls for a more global analysis that looks simultaneously at all of these correlations to find the causal factors. Has anyone attempted that?

Regarding the truth or falsity of H0: Perhaps it would be better if H0 were called a "reference hypothesis"? Statistics is fundamentally about whether data has the power to resolve differences among multiple reference hypotheses. It seems to me that asking for any hypothesis to be "true" is fundamental misguided, and this is one of the ontological flaws of the Bayesian approach.

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