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Measurement of Statistical Evidence: Picking Up Where Hacking and Others Left Off

Published online by Cambridge University Press:  01 January 2022

Abstract

Hacking’s Law of Likelihood says—paraphrasing—that data support hypothesis H1 over hypothesis H2 whenever the likelihood ratio (LR) for H1 over H2 exceeds 1. But Hacking later noted a seemingly fatal flaw in the LR itself: it cannot be interpreted as the degree of “evidential significance” across applications. I agree with Hacking about the problem, but I do not believe the condition is incurable. I argue here that the LR can be properly calibrated with respect to the underlying evidence, and I sketch the rudiments of a methodology for so doing.

Type
Evidence and Inference
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

This work was supported by a grant from the W. M. Keck Foundation. I thank Sang-Cheol Seok and Susan E. Hodge for helpful comments on an earlier draft.

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