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Error Probabilities in Error

Published online by Cambridge University Press:  01 April 2022

Colin Howson
Colin Howson*
Affiliation:
London School of Economics
*
†Department of Philosophy, London School of Economics, Houghton Street, London WC2A 2AE, England.
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Abstract

The Bayesian theory is outlined and its status as a logic defended. In this it is contrasted with the development and extension of Neyman-Pearson methodology by Mayo in her recently published book (1996). It is shown by means of a simple counterexample that the rule of inference advocated by Mayo is actually unsound. An explanation of why error-probablities lead us to believe that they supply a sound rule is offered, followed by a discussion of two apparently powerful objections to the Bayesian theory, one concerning old evidence and the other optional stopping.

Information

Type
Symposium: Philosophy of Statistics and Epistemology of Experiment: Bayesian vs. Error Statistical Approaches
Information
Philosophy of Science , Volume 64 , Issue S4: Supplement. Proceedings of the 1996 Biennial Meetings of the Philosophy of Science Association. Part II: Symposia Papers Dec., 1997 , 1997 , pp. S185 - S194
Copyright
Copyright © Philosophy of Science Association 1997

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Footnotes

‡

Thanks are due to Lawrence Jackson, Milo Schield, and Peter Urbach for their help, and to the British Academy for financial assistance.

References

BMJ (1996), British Medical Journal 313: 569.10.1136/bmj.313.7057.569CrossRefGoogle Scholar
Earman, J. (1992), Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. Cambridge, MA: MIT Press.Google Scholar
Garber, D. (1983), “Old Evidence and Logical Omniscience in Bayesian Confirmation Theory”, in Earman, J. (ed.), Testing Scientific Theories. Minneapolis: University of Minnesota Press, pp. 99–131.Google Scholar
Glymour, C. (1980), Theory and Evidence. Princeton: Princeton University Press.Google Scholar
Good, I.J. (1983), Good Thinking: The Foundations of Probability and its Applications. Minneapolis: University of Minnesota Press.Google Scholar
Howson, C. and Urbach, P. (1993), Scientific Reasoning: The Bayesian Approach. 2nd ed. Chicago: Open Court.Google Scholar
Kadane, J.B., Schervish, M. J., and Seidenfeld, T. (1996), “When Several Bayesians Agree that There Will Be No Reasoning to a Foregone Conclusion”, Philosophy of Science 63 (Proceedings): S281–S289.10.1086/289962CrossRefGoogle Scholar
Korb, K. (1991), “Explaining Science”, British Journal for the Philosophy of Science 42: 239–253.10.1093/bjps/42.2.239CrossRefGoogle Scholar
Mayo, D. (1996), Error and the Growth of Experimental Knowledge. Chicago: University of Chicago Press.10.7208/chicago/9780226511993.001.0001CrossRefGoogle Scholar
Popper, K.R. (1972), Objective Knowledge. Oxford: Clarendon Press.Google Scholar
Rosenkrantz, R. (1977), Inference, Method and Decision: Toward A Bayesian Philosophy of Science. Dordrecht: Reidel.10.1007/978-94-010-1237-9CrossRefGoogle Scholar
Savage, L.J. (ed.), (1962), The Foundations of Statistical Inference: A Discussion. London: Methuen.Google Scholar
Schield, M. (1996), “Using Bayesian Inference in Classical Hypothesis Testing”. Proceedings of the Statistical Education Section. Washington, D.C.: American Statistical Association.Google Scholar
Seidenfeld, T. (1979), “Why I am not an Objective Bayesian; Some Reflections Prompted by Rosenkrantz”, Theory and Decision 11: 413–440.10.1007/BF00139451CrossRefGoogle Scholar