Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-12-02T19:26:14.917Z Has data issue: false hasContentIssue false
  • English
  • Français

Methodology in Practice: Statistical Misspecification Testing

Published online by Cambridge University Press:  01 January 2022

Deborah G. Mayo  and
Aris Spanos
Get access Rights & Permissions [Opens in a new window]

Abstract

The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psychology, ecology, biology, risk assessment. Philosophical scrutiny can help disentangle ‘practical’ problems of model validation, and conversely, a methodology of statistical model validation can shed light on a number of issues of interest to philosophers of science.

Type
Methodology in Practice: Is There a New Normativity in Philosophy of Science?
Information
Philosophy of Science , Volume 71 , Issue 5: Proceedings of the 2002 Biennial Meeting of The Philosophy of Science Association. Part II: Symposia Papers , December 2004 , pp. 1007 - 1025
Copyright
Copyright © 2004 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

Forster, Malcolm R. (2001), “The New Science of Simplicity”, in Zellner, Arnold, Keuzenkamp, Hugo A., and McAleer, Michael (eds.), Simplicity, Inference and Modelling. Cambridge: Cambridge University Press, 83–119.Google Scholar
Forster, Malcolm R., and Sober, Elliott (1994), “How to Tell When Simpler, More Unified, or Less ad Hoc Theories Will Provide More Accurate Predictions”, How to Tell When Simpler, More Unified, or Less ad Hoc Theories Will Provide More Accurate Predictions 45: 1–35.Google Scholar
Glymour, Clark (1997), “A Review of Recent Work on the Foundations of Causal Inference”, in McKim and Turner (1997).Google Scholar
Levene, Howard (1952), “On the Power Function of Tests of Randomness Based on Runs Up and Down”, On the Power Function of Tests of Randomness Based on Runs Up and Down 23:34–56.Google Scholar
Mayo, Deborah G. (1991), “Novel Evidence and Severe Tests”, Novel Evidence and Severe Tests 58:523–552.Google Scholar
Mayo, Deborah G. (1996), Error and the Growth of Experimental Knowledge. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Mayo, Deborah G., and Spanos, Aris (2000), “A Post-data Interpretation of Neyman-Pearson Methods Based on a Conception of Severe Testing”, Measurements in Physics and Economics Discussion Paper Series, History and Methodology of Economics Group. London School of Economics and Political Science, Tymes Court, London.Google Scholar
McKim, Vaughn R., and Turner, Stephen P. (eds.) (1997), Causality in Crisis? Statistical Methods and the Search for Causal Knowledge in the Social Sciences. Notre Dame, IN: University of Notre Dame Press.Google Scholar
Mizon, Graham E. (1995), “A Simple Message for Autocorrelation Correctors: Don’t”, A Simple Message for Autocorrelation Correctors: Don’t 69:267–288.Google Scholar
Spanos, Aris (1986), Statistical Foundations of Econometric Modelling. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Spanos, Aris (1989), “On Re-reading Haavelmo: A Retrospective View of Econometric Modeling”, On Re-reading Haavelmo: A Retrospective View of Econometric Modeling 5:405–429.Google Scholar
Spanos, Aris (1995), “On Theory Testing in Econometrics: Modeling with Nonexperimental Data”, On Theory Testing in Econometrics: Modeling with Nonexperimental Data 67:189–226.Google Scholar
Spanos, Aris (1999), Probability Theory and Statistical Inference: Econometric Modeling with Observational Data. Cambridge: Cambridge University Press.Google Scholar
Spanos, Aris (2000), “Revisiting Data Mining: ‘Hunting’ with or without a License”, Revisiting Data Mining: ‘Hunting’ with or without a License 7:231–264.Google Scholar
Spirtes, Peter, Glymour, Clark, and Scheines, Richard (2001), Causation, Prediction, and Search, 2nd ed. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Woodward, James (1988), “Understanding Regression”, in Fine, Arthur and Leplin, Jarrett (eds.), PSA 88, Vol. 1. East Lansing, MI: Philosophy of Science Association, 255–269.Google Scholar
Yule, Udny (1895), “On the Correlation of Total Pauperism and the Proportion of Out Relief”, On the Correlation of Total Pauperism and the Proportion of Out Relief 5:603–611.Google Scholar