Verisimilitude, cross classification and prediction logic. Approaching the statistical truth by falsified qualitative theories

Mind and Society 6 (1):91-114 (2007)
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Abstract

In this paper it is argued that qualitative theories (Q-theories) can be used to describe the statistical structure of cross classified populations and that the notion of verisimilitude provides an appropriate tool for measuring the statistical adequacy of Q-theories. First of all, a short outline of the post-Popperian approaches to verisimilitude and of the related verisimilitudinarian non-falsificationist methodologies (VNF-methodologies) is given. Secondly, the notion of Q-theory is explicated, and the qualitative verisimilitude of Q-theories is defined. Afterwards, appropriate measures for the statistical verisimilitude of Q-theories are introduced, so to obtain a clear formulation of the intuitive idea that the statistical truth about cross classified populations can be approached by falsified Q-theories. Finally, it is argued that some basic intuitions underlying VNF-methodologies are shared by the so-called prediction logic, developed by the statisticians and social scientists David K. Hildebrand, James D. Laing and Howard Rosenthal

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References found in this work

Objective knowledge.Karl Raimund Popper - 1972 - Oxford,: Clarendon Press.
Conjectures and Refutations.K. Popper - 1963 - Les Etudes Philosophiques 21 (3):431-434.
The Logic of Scientific Discovery.K. Popper - 1959 - British Journal for the Philosophy of Science 10 (37):55-57.
Objective Knowledge.K. R. Popper - 1972 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 4 (2):388-398.

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