Mind & Society

, Volume 6, Issue 1, pp 91–114 | Cite as

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

Symposium Article

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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Copyright information

© Fondazione Rosselli 2007

Authors and Affiliations

  1. 1.Dipartimento di FilosofiaUniversità di TriesteTriesteItaly

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