The philosophy of exploratory data analysis

Philosophy of Science 50 (2):283-295 (1983)

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
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DOI 10.1086/289110
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Philosophical Issues in Neuroimaging.Colin Klein - 2010 - Philosophy Compass 5 (2):186-198.
The Analysis of Data and the Evidential Scope of Neuroimaging Results.Jessey Wright - 2018 - British Journal for the Philosophy of Science 69 (4):1179-1203.

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