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Measured Realism and Statistical Inference: An Explanation for the Fast Progress of “Hard” Psychology

Published online by Cambridge University Press:  01 April 2022

J. D. Trout
J. D. Trout*
Affiliation:
Loyola University of Chicago
*
†Philosophy Department and the Parmly Hearing Institute, Loyola University of Chicago, 6525 North Sheridan Road, Chicago, IL 60626.
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Abstract

The use of null hypothesis significance testing (NHST) in psychology has been under sustained attack, despite its reliable use in the notably successful, so-called “hard” areas of psychology, such as perception and cognition. I argue that, in contrast to merely methodological analyses of hypothesis testing (in terms of “test severity,” or other confirmation-theoretic notions), only a patently metaphysical position can adequately capture the uneven but undeniable successes of theories in “hard psychology.” I contend that Measured Realism satisfies this description, and characterizes the role of NHST in hard psychology.

Information

Type
Philosophy of Psychology and Cognitive Science
Information
Philosophy of Science , Volume 66 , Issue S3: Supplement. Proceedings of the 1998 Biennial Meetings of the Philosophy of Science Association. Part I: Contributed Papers Sep., 1999 , 1999 , pp. S260 - S272
Copyright
Copyright © 1999 by the Philosophy of Science Association

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References

Bacon, F. (1960), The New Organon & Related Writings. Edited by Anderson, F. H. Indianapolis, IN: Bobbs-Merrill.Google Scholar
Boyd, R. (1983), “On the Current Status of Scientific Realism”, in Boyd, R., Gasper, P., and Trout, J. D. (eds.), The Philosophy of Science. Cambridge, MA: MIT Press, 195–222.Google Scholar
Dawes, R. (1988), Rational Choice in an Uncertain World. New York: Harcourt, Brace, Jovanovich.Google Scholar
Duhem, P. (1954), The Aim and Structure of Physical Theory. Translated by Wiener, P. New York: Atheneum.CrossRefGoogle Scholar
Giere, R. (1984), Understanding Scientific Reasoning, 2nd ed. New York: Holt, Rinehart & Winston.Google Scholar
Gilovich, T. (1991), How We Know What Isn't So. New York: Free Press.Google Scholar
Harlow, L., Mulaik, S., and Steiger, J. (eds.) (1997), What If There Were No Significance Tests?. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Horwich, P. (1991), “On the Nature and Norms of Theoretical Commitment”, Philosophy of Science 58: 1–14.CrossRefGoogle Scholar
Kosso, P. (1989), “Science and Objectivity”, The Journal of Philosophy 86: 245–257.10.2307/2027109CrossRefGoogle Scholar
Loftus, G. (1996), “Psychology Will be a Much Better Science When We Change the Way We Analyze Data”, Psychological Science 5(6): 161–171.Google Scholar
Meehl, P. (1997), “The Problem is Epistemology, Not Statistics: Replace Significance Tests by Confidence Intervals and Quantify Accuracy of Risky Numerical Predictions”, in Harlow, Mulaik, and Steiger 1997, 393–425.Google Scholar
Piatelli-Palmarini, P. (1994), Inevitable Illusions. New York: Wiley.Google Scholar
Popper, K. (1962), Conjectures and Refutations. New York: Basic Books.Google Scholar
Rindskopf, D. (1997), “Testing ‘Small’, Not Null, Hypotheses: Classical and Bayesian Approaches”, in Harlow, Mulaik, and Steiger 1997, 319–332.Google Scholar
Todes, D. T. (1997), “Pavlov's Physiology Factory”, Isis 88: 205–246.10.1086/383690CrossRefGoogle ScholarPubMed
Trout, J. D. (1991), “Belief Attribution in Science: Folk Psychology Under Theoretical Stress” Synthese 87: 379–400.CrossRefGoogle Scholar
Trout, J. D. (1998), Measuring the Intentional World: Realism, Naturalism, and Quantitative Methods in the Behavioral Sciences. New York: Oxford University Press.10.1093/0195107667.001.0001CrossRefGoogle Scholar