David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Philosophy of Science 74 (5):701-711 (2007)
The Variety of Evidence Thesis (VET) says that (ceteris paribus) the more diverse (or varied) of two bodies of evidence is the more confirmatory of a hypothesis H. Two recent types of Bayesian explication of VET account for the intuitive force of VET by defining variety as some function of the probabilities of the propositions which collectively constitute a body of evidence. I show that these two accounts of VET are not tracking a meaningful property of bodies of evidence, but rather are tracking artifacts of how those bodies of evidence are described. According to each account, whether a body of evidence is more varied than another depends on how the bodies are split into parts. Furthermore, for each type of account there exists a way to redescribe the total evidence such that any two totalities are equally varied. ‡I would like to thank Elliott Sober for comments on multiple drafts of this paper, and Malcolm Forster, John Koolage, and Joel Velasco for comments on a verbal delivery. †To contact the author, please write to: Department of Philosophy, University of Wisconsin-Madison, 5185 Helen C. White Hall, 600 North Park Street, Madison, WI 53706; e-mail: firstname.lastname@example.org.
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References found in this work BETA
John Earman (1992). Bayes or Bust? Bradford.
David Miller (1974). Popper's Qualitative Theory of Verisimilitude. British Journal for the Philosophy of Science 25 (2):166-177.
Wayne C. Myrvold (2003). A Bayesian Account of the Virtue of Unification. Philosophy of Science 70 (2):399-423.
Branden Fitelson (2001). A Bayesian Account of Independent Evidence with Applications. Proceedings of the Philosophy of Science Association 2001 (3):S123-.
Citations of this work BETA
François Claveau (2011). Evidential Variety as a Source of Credibility for Causal Inference: Beyond Sharp Designs and Structural Models. Journal of Economic Methodology 18 (3):233-253.
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