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The Philosophy of Exploratory Data Analysis

Published online by Cambridge University Press:  01 April 2022

I. J. Good
I. J. Good*
Affiliation:
Statistics Department Virginia Polytechnic Institute and State University
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Abstract

This paper attempts to define Exploratory Data Analysis (EDA) more precisely than usual, and to produce the beginnings of a philosophy of this topical and somewhat novel branch of statistics.

A data set is, roughly speaking, a collection of k-tuples for some k. In both descriptive statistics and in EDA, these k-tuples, or functions of them, are represented in a manner matched to human and computer abilities with a view to finding patterns that are not “kinkera”. A kinkus is a pattern that has a negligible probability of being even partly potentially explicable. A potentially explicable pattern is one for which there probably exists a hypothesis of adequate “explicativity”, which is another technical probabilistic concept. A pattern can be judged to be probably potentially explicable even if we cannot find an explanation. The theory of probability understood here is one of partially ordered (interval-valued), subjective (personal) probabilities. Among other topics relevant to a philosophy of EDA are the “reduction” of data; Francis Bacon's philosophy of science; the automatic formulation of hypotheses; successive deepening of hypotheses; neurophysiology; and rationality of type II.

Information

Type
Research Article
Information
Philosophy of Science , Volume 50 , Issue 2 , June 1983 , pp. 283 - 295
Copyright
Copyright © 1983 by the Philosophy of Science Association

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Footnotes

†

I am grateful to John W. Pratt for some useful criticisms. This work was supported in part by N.I.H. Grant R01-GM18770.

References

Anscombe, F. J. (1967), “Topics in the investigation of linear relations fitted by the method of least squares” (with discussion), Journal of the Royal Statistical Society B 29: 1–52.Google Scholar
Cochran, W. G. (1972), “Observational studies”, in Statistical Papers in Honor of George W. Snedecor. Ames, Iowa: Iowa State University Press, pp. 70–90.Google Scholar
Cox, D. R. (1978), “Some remarks on the role of statistics of graphical methods”, Journal of the Royal Statistical Society, Series C (Applied Statistics) 27: 4–9.Google Scholar
Daniel, Cuthbert (1978), “Patterns in residuals in the two-way layout”, Technometrics 20: 385–395.CrossRefGoogle Scholar
de Groot, A. D. (1965), Thought and Choice in Chess, Baylor, G. W. (ed.) The Hague & Paris: Mouton. A translation, with additions, of a Dutch version of 1946.Google Scholar
Efron, B. (1971), “Does an observed sequence of numbers follow a simple rule? (Another look at Bode's law.)”, Journal of the American Statistical Association 66: 552–568 (with discussion).CrossRefGoogle Scholar
Freund, R. J., Vail, R. W., & Clunies-Ross, C. W. (1961), “Residual analysis”, Journal of the American Statistical Association 56: 98–104.CrossRefGoogle Scholar
Ghiselin, B. (ed.) (1952), The Creative Process. New York: New American Library.Google Scholar
Good, I. J. (1950), Probability and the Weighing of Evidence. London: Charles Griffin; New York: Hafners.Google Scholar
Good, I. J. (1958), “The interaction algorithm and practical Fourier analysis”, Journal of the Royal Statistical Society, Series B 20: 361–372.Google Scholar
Good, I. J. (1959), “Could a machine make probability judgments?” Computers and Automation 8: 14–16 and 24–26.Google Scholar
Good, I. J. (1963), “Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables”, Annals of Mathematical Statistics 34: 911–934.CrossRefGoogle Scholar
Good, I. J. (1969), “A subjective analysis of Bode's law and an ‘objective’ test for approximate numerical rationality”, Journal of the American Statistical Association 64: 23–66 (with discussion).Google Scholar
Good, I. J. (1977a), “Explicativity: a mathematical theory of explanation with statistical applications”, Proceedings of the Royal Society A, 354. London: 303–330. Also in Studies in Bayesian Econometrics and Statistics in Honor of Harold Jeffreys, Arnold Zellner (ed.) Amsterdam: North Holland, 1980; and, in part, in Good Thinking: the Foundations of Probability and its Applications. Minneapolis: University of Minnesota Press; 1983.Google Scholar
Good, I. J. (1977b), “Summing up of the discussion on inductive inference”, Machine Intelligence 8, E. W. Elcock and D. Michie (eds). Chichester: Ellis Horwood Ltd. & New York: Wiley, pp. 205–206.Google Scholar
Good, I. J. (1978), “Adenine arabinoside therapy”, Journal of Statistical Computational Simulation 6: 314–315.Google Scholar
Good, I. J. (1980), “Some logic and history of hypothesis testing”, in Philosophical Foundations of Economics, Pitt, J. C. (ed.). Dordrecht: D. Reidel.Google Scholar
Good, I. J. (1981), “The philosophy of exploratory datum analysis”, American Statistical Association: 1980 Proceedings of the Business and Economic Statistics Section. Washington, DC: American Statistical Association, pp. 1–7.Google Scholar
Hadamard, J. (1949), The Psychology of Invention in the Mathematical Field. Princeton: University Press. Reprinted by Dover Publications, New York, 1954.Google Scholar
Jaynes, E. T. (1957), “Information theory and statistical mechanics”, Physical Reviews 106: 620–630.CrossRefGoogle Scholar
Keynes, J. M. (1921), A Treatise on Probability. London: Macmillan.Google Scholar
Kruskal, W. H. (1982), “Criteria for judging statistical graphics”, Utilitas Mathematica 21B: 283–310.Google Scholar
Learner, E. E. (1978), Specification Searches: Ad Hoc Inferences with Nonexperimental Data. New York: Wiley.Google Scholar
Michalski, R. S. & Negri, P. (1977), “An experiment on inductive learning in chess end games”, In Machine Intelligence 8, Elcock, E. W. & Michie, D. (eds.). Chichester: Ellis Horwood & New York: Wylie, pp. 175–192.Google Scholar
Nieto, M. M. (1972), The Titius-Bode Law of Planetary Distances. New York: Pergamon Press.Google Scholar
Ornstein, R. E. (1973), The Nature of Human Consciousness. San Francisco: Freeman.Google Scholar
Russell, Bertrand (1946), History of Western Philosophy. London: George Allen and Unwin.Google Scholar
Tukey, J. W. (1977), Exploratory Data Analysis. Reading, Mass.: Addison-Wesley.Google Scholar
Wainer, H. & Francolini, C. M. (1980), “An empirical inquiry concerning human understanding of two-variable color maps”, American Statistician 34: 81–93.Google Scholar
Whittaker, E. T. & Robinson, G. (1944), The Calculus of Observations (4th edn.). London: Blackie; New York: Dover reprint 1967.Google Scholar