Skip to main content

Part of the book series: Springer Graduate Texts in Philosophy ((SGTP,volume 1))


The aim of confirmation theory is to provide a true account of the principles that guide scientific argument in so far as that argument is not, and does not purport to be, of a deductive kind. A confirmation theory should serve as a critical and explanatory instrument quite as much as do theories of deductive inference. Any successful confirmation theory should, for example, reveal the structure and fallacies, if any, in Newton’s argument for universal gravitation, in nineteenth-century arguments for and against the atomic theory, in Freud’s arguments for psychoanalytic generalizations. Where scientific judgements are widely shared, and sociological factors cannot explain their ubiquity, and analysis through the lens provided by confirmation theory reveals no good explicit arguments for the judgements, confirmation theory ought at least sometimes to suggest some good arguments that may have been lurking misperceived. Theories of deductive inference do that much for scientific reasoning in so far as that reasoning is supposed to be demonstrative. We can apply quantification theory to assess the validity of scientific arguments, and although we must almost always treat such arguments as enthymematic, the premisses we interpolate are not arbitrary; in many cases, as when the same subject-matter is under discussion, there is a common set of suppressed premisses. Again, there may be differences about the correct logical form of scientific claims; differences of this kind result in (or from) different formalizations, for example, of classical mechanics. But such differences often make no difference for the assessment of validity in actual arguments. Confirmation theory should do as well in its own domain. If it fails, then it may still be of interest for many purposes, but not for the purpose of understanding scientific reasoning.

Who cares whether a pig-farmer is a Bayesian?—R. C. Jeffrey.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.

    A third view, that probabilities are to be understood exclusively as frequencies, has been most ably defended by Wesley Salmon (1969).

  2. 2.

    More detailed accounts of means for determining degrees of belief may be found in Jeffrey (1965). It is a curious fact that the procedures that Bayesians use for determining subjective degrees of belief empirically are an instance of the general strategy described in Glymour 1981, ch. 5. Indeed, the strategy typically used to determine whether or not actual people behave as rational Bayesians involves the bootstrap strategy described in that chapter.

  3. 3.

    For further criticisms of the Dutch-book argument see Kyburg 1978.

  4. 4.

    Moreover, I believe that much of her discussion of methodological principles has only the loosest relation to Bayesian principles.

  5. 5.

    This is the account suggested by Horwich (1978).

  6. 6.

    All of the defences sketched below were suggested to me by one or another philosopher sympathetic to the Bayesian view; I have not attributed the arguments to anyone for fear of misrepresenting them. None the less, I thank Jon Dorling, Paul Teller, Daniel Garber, Ian Hacking, Patrick Suppes, Richard Jeffrey, and Roger Rosencrantz for valuable discussions and correspondence on the point at issue.

  7. 7.

    The actual history is still more complicated. Newcomb and Doolittle obtained values for the anomaly differing by about 2 s of are per century. Early in the 1920s. Grossmann discovered that Newcomb had made an error in calculation of about that magnitude.

  8. 8.

    Around 1900 is fanciful, before general relativity is not. In 1914 E. Freundlich mounted an expedition to Russia to photograph the eclipse of that year in order tο determine the gravitational deflection of starlight. At that time, Einstein had predicted an angular deflection for light passing near the limb of the sun that was equal in value to that derived from Newtonian principles by Soldner in 1801. Einstein did not obtain the field equations that imply a value for the deflection equal to twice the Newtonian value until late in 1915. Freundlich was caught in Russia by the outbreak of World War I, and was interned there. Measurement of the deflection had to wait until 1919.


  • Carnap, R. (1950). The logical foundations of probability. Chicago: University of Chicago Press.

    Google Scholar 

  • Glymour, C. (1981). Theory and evidence. Chicago: University of Chicago Press.

    Google Scholar 

  • Hesse, M. (1974). The structure of scientific inference. Berkeley: University of California Press.

    Google Scholar 

  • Horwich, P. (1978). An appraisal of Glymour’s confirmation theory. Journal of Philosophy, 75, 98–113.

    Article  Google Scholar 

  • Jeffrey, R. (1965). The logic of decision. New York: McGraw-Hill.

    Google Scholar 

  • Jeffreys, H. (1967). Theory of probability. Oxford: Clarendon.

    Google Scholar 

  • Jeffreys, H. (1973). Scientific inference. Cambridge: Cambridge University Press.

    Google Scholar 

  • Kyburg, H. (1978). Subjective probability: Criticisms, reflections and problems. Journal of Philosophical Logic, 7, 157–180.

    Article  Google Scholar 

  • Putnam, H. (1967). Probability and confirmation. In S. Morgenbesser (Ed.), Philosophy of science today. New York: Basic Books.

    Google Scholar 

  • Rosencrantz, R. (1976). Simplicity. In W. Harper & C. Hooker (Eds.), Foundations and philosophy of statistical inference. Boston: Reidel.

    Google Scholar 

  • Salmon, W. C. (1969). Foundations of scientific inference. Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Savage, L. (1972). The foundations of statistics. New York: Dover.

    Google Scholar 

  • Shimony, A. (1970). Scientific inference. In R. G. Colodny (Ed.), The nature and function of scientific theories (pp. 79–179). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Clark Glymour .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Glymour, C. (2016). Why I am not a Bayesian. In: Arló-Costa, H., Hendricks, V., van Benthem, J. (eds) Readings in Formal Epistemology. Springer Graduate Texts in Philosophy, vol 1. Springer, Cham.

Download citation

Publish with us

Policies and ethics