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

Mind and Society 6 (1):91-114 (2007)
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
Keywords Verisimilitude  Statistics  Cross classification  Prediction logic  Qualitative theories  Non-falsificationist methodologies  Popper  Lakatos
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DOI 10.1007/s11299-006-0022-2
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References found in this work BETA
K. R. Popper (1966). Conjectures and Refutations. Les Etudes Philosophiques 21 (3):431-434.
Ilkka Niiniluoto (1989). Truthlikeness. Journal of Symbolic Logic 54 (1):297-300.

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