Authors
Samuel C. Fletcher
University of Minnesota
Abstract
The likelihood principle is typically understood as a constraint on any measure of evidence arising from a statistical experiment. It is not sufficiently often noted, however, that the LP assumes that the probability model giving rise to a particular concrete data set must be statistically adequate—it must “fit” the data sufficiently. In practice, though, scientists must make modeling assumptions whose adequacy can nevertheless then be verified using statistical tests. My present concern is to consider whether the LP applies to these techniques of model verification. If one does view model verification as part of the inferential procedures that the LP intends to constrain, then there are certain crucial tests of model verification that no known method satisfying the LP can perform. But if one does not, the degree to which these assumptions have been verified is bracketed from the evidential evaluation under the LP. Although I conclude from this that the LP cannot be a universal constraint on any measure of evidence, proponents of the LP may hold out for a restricted version thereof, either as a kind of “ideal” or as defining one among many different forms of evidence.
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Error and the Growth of Experimental Knowledge.Deborah Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
Venetian Sea Levels, British Bread Prices, and the Principle of the Common Cause.Elliott Sober - 2001 - British Journal for the Philosophy of Science 52 (2):331-346.

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