Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-24T06:13:13.708Z Has data issue: false hasContentIssue false
  • English
  • Français

Error Statistics and Learning From Error: Making a Virtue of Necessity

Published online by Cambridge University Press:  01 April 2022

Deborah G. Mayo
Deborah G. Mayo*
Affiliation:
Virginia Tech
*
†Department of Philosophy, Virginia Tech, Blacksburg, VA 24061; [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

The error statistical account of testing uses statistical considerations, not to provide a measure of probability of hypotheses, but to model patterns of irregularity that are useful for controlling, distinguishing, and learning from errors. The aim of this paper is (1) to explain the main points of contrast between the error statistical and the subjective Bayesian approach and (2) to elucidate the key errors that underlie the central objection raised by Colin Howson at our PSA 96 Symposium.

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. S195 - S212
Copyright
Copyright © Philosophy of Science Association 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

‡

I thank E. L. Lehmann for several important error statistical insights.

References

Hacking, I. (1992), “The Self-Vindication of the Laboratory Sciences”, in Pickering, A. (ed.), Science as Practice and Culture. Chicago: University of Chicago Press, pp. 29–64.Google Scholar
Howson, C. (1997), “Error Probabilities in Error”, Philosophy of Science 64 (Proceedings): this issue.CrossRefGoogle Scholar
Howson, C. and Urbach, P. (1989), Scientific Reasoning: The Bayesian Approach. La Salle: Open Court.Google Scholar
Mayo, D. (1985), “Increasing Public Participation in Controversies Involving Hazards: The Value of Metastatistical Rules”, Science, Technology, and Human Values 10: 55–68.CrossRefGoogle Scholar
Mayo, D. (1989), “Toward a More Objective Understanding of the Evidence of Carcinogenic Risk”, in Fine, A. and Leplin, J. (eds.), PSA 1988, vol. 2, East Lansing, MI: Philosophy of Science Association, pp. 489–503.Google Scholar
Mayo, D. (1996), Error and the Growth of Experimental Knowledge. Chicago: The University of Chicago Press.CrossRefGoogle Scholar
Mayo, D. (1997), “Duhem's Problem, The Bayesian Way, and Error Statistics, or ‘What's Belief Got To Do With It‘?” and “Response to Howson and Laudan”, Philosophy of Science (June 1997), in press.CrossRefGoogle Scholar
Neyman, J. (1952), Lectures and Conferences on Mathematical Statistics and Probability, 2nd ed. Washington, DC: U.S. Department of Agriculture.Google Scholar
Neyman, J. (1971), “Foundations of Behavioristic Statistics”, in Godambe, V. P. and Sprott, D. A. (eds.), Foundations of Statistical Inference. Toronto: Holt, Rinehart and Winston of Canada, pp.1–13 (comments and reply, pp. 14–19).Google Scholar
Neyman, J. (1977), “Frequentisi Probability and Frequentist Statistics”, Synthese Synthese: 36–97. Newsweek, (February 24, 1997), “The Mammogram War”, pp. 54–58.Google Scholar
Pearson, E. S. (1950), “On Questions Raised by the Combination of Tests Based on Discontinuous Distributions”, Biometrika 37: 383–398, as reprinted in E. S. Pearson (1966), The Selected Papers of E.S. Pearson. Berkeley: University of California Press, pp. 217–232.10.1093/biomet/37.3-4.383CrossRefGoogle ScholarPubMed
Salmon, W. (1991), “The Appraisal of Theories: Kuhn Meets Bayes”, in Fine, A., Forbes, M., and Wessels, L. (eds.), PSA 1990, vol. 2. East Lansing, MI: Philosophy of Science Association, pp. 325–332.Google Scholar
Suppes, P. (1969), “Models of Data”, in his Studies in the Methodology and Foundations of Science. Dordrecht: D. Reidel, pp. 24–35.CrossRefGoogle Scholar