Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-26T05:15:51.650Z Has data issue: false hasContentIssue false

Experimental Practice and an Error Statistical Account of Evidence

Published online by Cambridge University Press:  01 April 2022

Deborah G. Mayo*
Affiliation:
Virginia Tech
*
Send requests for reprints to the author, Department of Philosophy, Virginia Tech, Major Williams Hall, Blacksburg, VA 24061.

Abstract

In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions in which a new and more adequate philosophy of evidence can move.

Type
Experiment and Conceptual Change
Copyright
Copyright © 2000 by the Philosophy of Science Association

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 gratefully acknowledge the cooperative efforts of my co-symposiasts, Peter Achinstein and James Woodward. Their willingness to exchange fruitful comments and questions on drafts of each others' papers, and to incorporate examples and ideas from each of our three papers in their own contributions, was a model of constructive progress and synergy.

References

Achinstein, Peter (1999), “Experimental Practice and the Reliable Detection of Errors”, this volume.Google Scholar
Buchwald, Jed 1994, The Creation of Scientific Effects. Chicago: University of Chicago Press.10.7208/chicago/9780226078915.001.0001CrossRefGoogle Scholar
Edwards, W., Lindman, H., and Savage, L. (1963), “Bayesian Statistical Inference for Psychological Research”, Psychological Review 70: 193242.CrossRefGoogle Scholar
Hacking, I. (1983), Representing and Intervening: Introductory Topics in the Philosophy of Natural Science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Mayo, D. (1996), Error and the Growth of Experimental Knowledge. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Mayo, D. (2000), “Theory Testing, Statistical Methodology and the Growth of Experimental Knowledge”, in the Proceedings of the International Congress for Logic, Methodology, and Philosophy of Science (Cracow, Poland, August 20–26, 1999). Dordrecht: Kluwer.Google Scholar
Mayo, D. and Kruse, M. (forthcoming), “Principles of Inference and their Consequences”.Google Scholar
Saulson, Peter (1994), Fundamentals of Interferometric Gravitational Wave Detectors. Singapore: World Scientific Publishing Co.CrossRefGoogle Scholar
Savage, L. J. (ed.) (1962), The Foundations of Statistical Inference: A Discussion. London: Methuen.Google Scholar
Spanos, A. (2000), “Revisting Data Mining: ‘Hunting’ With or Without a Licence”. Journal of Economic Methodology 7.10.1080/13501780050045119CrossRefGoogle Scholar
Taylor, Joseph (1992), “Testing Relativistic Gravity with Binary and Millisecond Pulsars”, in Gleiser, R. J., Kozameh, C. N., and Moreschi, O. M. (eds.), General Relativity and Gravitation 1992, Proceedings of the Thirteenth International Conference on General Relativity and Gravitation held at Cordoba, Argentina, 28 June-4 July 1992. Bristol: Institute of Physics Publishing.Google Scholar
Woodward, James (1999), “Data, Phenomena and Reliability”, this volume.Google Scholar