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


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.


Verisimilitude Statistics Cross classification Prediction logic Qualitative theories Non-falsificationist methodologies Popper Lakatos 


  1. Cohen LJ (1973) The paradox of anomaly. In: Bogdan RJ, Niiniluoto I (eds) Logic, language, and probability. Reidel, Dordrecht, pp 78–82Google Scholar
  2. Cohen LJ (1977) The probable and the provable. Oxford University Press, OxfordGoogle Scholar
  3. Cohen LJ (1989) An introduction to the philosophy of induction and probability. Clarendon, OxfordGoogle Scholar
  4. Coleman JS (1964) Introduction to mathematical sociology. Free Press, New YorkGoogle Scholar
  5. Costner HL (1965) Criteria for measures of association. Am Soc Rev 30:341–353CrossRefGoogle Scholar
  6. Duverger M (1954) Political parties. Wiley, New YorkGoogle Scholar
  7. Festa R (1987) Theory of similarity, similarity of theories, and verisimilitude. In: Kuipers TK (ed) What is closer-to-the-truth?. Rodopi, Amsterdam, pp 145–176Google Scholar
  8. Festa R (2007) Verisimilitude, qualitative theories, and statistical inferences. In: Sintonen M, Pihlström S, Raatikainen P (eds) Festschrift for Ilkka Niiniluoto (forthcoming)Google Scholar
  9. Goodman LA, Kruskal W (1974a) Empirical valuation of formal theory. J Math Soc 3:187–196CrossRefGoogle Scholar
  10. Goodman LA, Kruskal W (1974b) More about empirical valuation of formal theory. J Math Soc 3:211–213CrossRefGoogle Scholar
  11. Hildebrand DK, Laing JD, Rosenthal H (1974a) Prediction logic: a method for empirical evaluation of formal theory. J Math Soc 3:163–185CrossRefGoogle Scholar
  12. Hildebrand DK, Laing JD, Rosenthal H (1974b). Prediction logic and quasi-independence in empirical evaluation of formal theory. J Math Soc 3:197–209CrossRefGoogle Scholar
  13. Hildebrand DK, Laing JD, Rosenthal H (1975) A prediction logic approach to causal models of qualitative variates. In: Heise DR (ed) (1976) Sociological methodology. JosseyBass, San Francisco, pp 146–175Google Scholar
  14. Hildebrand DK, Laing JD, Rosenthal H (1976a) Prediction analysis in political research. Am Polit Rev 70:509–535CrossRefGoogle Scholar
  15. Hildebrand DK, Laing JD, Rosenthal H (1976b) Prediction analysis of cross classification. Wiley, New YorkGoogle Scholar
  16. Hildebrand DK, Laing JD, Rosenthal H (1977) Analysis of ordinal data. Sage Publication, Newbury ParkGoogle Scholar
  17. Homans GC (1961) Social behavior: its elementary forms. Harcourt Brace Jovanovich, New YorkGoogle Scholar
  18. Kuipers TAF (ed) (1987) What is closer-to-the-truth? Rodopi, AmsterdamGoogle Scholar
  19. Kuipers TAF (2000) From instrumentalism to constructive realism. Kluwer, DordrechtGoogle Scholar
  20. Kuipers TAF (2001) Structures in science. Kluwer, DordrechtGoogle Scholar
  21. Lakatos I (1968) Changes in the problem of inductive logic. In: Lakatos I (ed) The problem of inductive logic. NorthHolland, Amsterdam, pp 315–417Google Scholar
  22. Lakatos I (1970) Falsificationism and the methodology of scientific research programmes. In: Lakatos I, Musgrave A (eds) Criticism and the growth of knowledge. Cambridge University Press, Cambridge, pp 91–196. Reprinted in: Lakatos I (1978) The methodology of scientific research programmes, edited by Worrall J, Currie G. Cambridge University Press, Cambridge, pp 8–101Google Scholar
  23. Lakatos I (1974) Popper on demarcation and induction. In: Schilpp PA (ed) The philosophy of Karl Popper. Open Court, La Salle, pp 241–273Google Scholar
  24. Lipset SM (1960) Political man: the social bases of politics. Doubleday, Garden CityGoogle Scholar
  25. Miller D (1974) Popper’s qualitative theory of verisimilitude. Br J Philos Sci 25:166–177CrossRefGoogle Scholar
  26. Niiniluoto I (1987) Truthlikeness. Reidel, DordrechtGoogle Scholar
  27. Niiniluoto I (1989) Corroboration, verisimilitude, and the success of science. In: Gavroglu K, Goudaroulis Y, Nicolacopoulos P (eds) Imre Lakatos and theories of scientific change. Reidel, Dordrecht, pp 229–243Google Scholar
  28. Niiniluoto I (1998) Verisimilitude: the third period. Br J Philos Sci 49:11–29CrossRefGoogle Scholar
  29. Oddie G (1986) Likeness to the truth. Reidel, DordrechtGoogle Scholar
  30. Popper KR (1959) The logic of scientific discovery, Hutchinson, London. (1st edn, 1934: Logik der Forschung, Wien)Google Scholar
  31. Popper KR (1963) Conjectures and refutations. Routledge and Kegan Paul, LondonGoogle Scholar
  32. Popper KR (1972) Objective knowledge. Clarendon Press, OxfordGoogle Scholar
  33. Tichý P (1974) On Popper’s definition of verisimilitude. Br J Philos Sci 25:155–160CrossRefGoogle Scholar
  34. Tuomela R (1978) Verisimilitude and theory-distance. Synthese 38:213–246CrossRefGoogle Scholar

Copyright information

© Fondazione Rosselli 2007

Authors and Affiliations

  1. 1.Dipartimento di FilosofiaUniversità di TriesteTriesteItaly

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