Measurement of Statistical Evidence: Picking Up Where Hacking and Others Left Off.
Hacking's Law of Likelihood says--paraphrasing--that data support hypothesis H1 over hypothesis H2 whenever the likelihood ratio (LR) for H1 over H 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 a...
| Publicado en: | Philosophy of Science Vol. 84; no. 5; pp. 853 - 866 |
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| Autor principal: | |
| Formato: | Artículo |
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Cambridge University Press
Dec2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | Hacking's Law of Likelihood says--paraphrasing--that data support hypothesis H1 over hypothesis H2 whenever the likelihood ratio (LR) for H1 over H 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. |
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