Mind & Society

, Volume 6, Issue 1, pp 91–114

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

Authors

    • Dipartimento di FilosofiaUniversità di Trieste
Symposium Article

DOI: 10.1007/s11299-006-0022-2

Cite this article as:
Festa, R. Mind & Society (2007) 6: 91. doi:10.1007/s11299-006-0022-2

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

VerisimilitudeStatisticsCross classificationPrediction logicQualitative theoriesNon-falsificationist methodologiesPopperLakatos

Copyright information

© Fondazione Rosselli 2007