Mind & Society

, Volume 6, Issue 1, pp 91–114

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 

Copyright information

© Fondazione Rosselli 2007

Authors and Affiliations

  1. 1.Dipartimento di FilosofiaUniversità di TriesteTriesteItaly

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