Hostname: page-component-848d4c4894-pjpqr Total loading time: 0 Render date: 2024-06-14T14:21:36.888Z Has data issue: false hasContentIssue false

The Confirmation of Quantitative Laws

Published online by Cambridge University Press:  01 April 2022

Henry E. Kyburg Jr.*
Affiliation:
Philosophy Department, University of Rochester

Abstract

Quantitative laws are more typical of science than are generalizations involving observational predicates, yet much discussion of scientific inference takes the confirmation of a universal generalization by its instances to be typical and paradigmatic. The important difference is that measurement necessarily involves error. It is argued that because of error laws can no more be refuted by observation than they can be verified by observation. Without much background knowledge, tests of a law mainly provide evidence for the distribution of errors of measurement of the quantities involved. With more background knowledge, the data may contribute either to our knowledge of the error distributions, or to the grounds we have for accepting or rejecting the law. With enough background knowledge, data may verify as well as refute laws.

Type
Research Article
Copyright
Copyright © 1985 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.)

References

Barford, N. C. (1967), Experimental Measurements: Precision, Error, and Truth. Reading: Addison-Wesley.Google Scholar
Bevington, Philip R. (1969), Data Reduction and Error Analysis for the Physical Sciences. New York: McGraw-Hill.Google Scholar
Clifford, A. A. (1973), Multivariate Error Analysis. New York and Toronto: John Wiley and Sons.Google Scholar
Duhem, Pierre (1954), The Aim & Structure of Physical Theory. Princeton: Princeton University Press.CrossRefGoogle Scholar
Glymour, Clark (1980), Theory and Evidence. Princeton: Princeton University Press.Google Scholar
Horwich, Paul (1982), Probability and Evidence. Cambridge: Cambridge University Press.Google Scholar
Klepikov, N. P., and Sokolov, S. (1961), Analysis and Planning of Experiments by the Method of Maximum Likelihood. Berlin: Akademie Verlag.Google Scholar
Krantz, David; Luce, Duncan; Suppes, Patrick; and Tuevsky, Amos (1971), Foundations of Measurement. New York: Academic Press.Google Scholar
Kyburg, Henry E., Jr, . (1976), The Logical Foundations of Statistical Inference. Dordrecht: D. Reidel.Google Scholar
Kyburg, Henry E., Jr, . (1983), Epistemology and Inference. Minneapolis: University of Minnesota Press.Google Scholar
Kyburg, Henry E., Jr, . (1984), Theory and Measurement. Cambridge: Cambridge University Press.Google Scholar
Smart, W. M. (1958), Combination of Observations. Cambridge: Cambridge University Press.Google Scholar
Wilson, E. Bright (1952), An Introduction to Scientific Research. New York and Toronto: McGraw-Hill.Google Scholar