David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Philosophy of Science 50 (2):283-295 (1983)
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
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Citations of this work BETA
D. Napoletani, M. Panza & D. Struppa (2011). Agnostic Science. Towards a Philosophy of Data Analysis. Foundations of Science 16 (1):1-20.
Colin Klein (2010). Philosophical Issues in Neuroimaging. Philosophy Compass 5 (2):186-198.
Brian D. Haig (1992). From Nuisance Variables to Explanatory Theories: A Reformulation of the Third Variable Problem. Educational Philosophy and Theory 24 (2):78–97.
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