David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Colin Klein (2010). Philosophical Issues in Neuroimaging. Philosophy Compass 5 (2):186-198.
D. Napoletani, M. Panza & D. Struppa (2011). Agnostic Science. Towards a Philosophy of Data Analysis. Foundations of Science 16 (1):1-20.
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.
Similar books and articles
Bjørn Hofmann, Anne Myhr & Søren Holm (2013). Scientific Dishonesty—a Nationwide Survey of Doctoral Students in Norway. BMC Medical Ethics 14 (1):1-9.
William F. Brewer & Clark A. Chinn (1994). Scientists' Responses to Anomalous Data: Evidence From Psychology, History, and Philosophy of Science. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:304 - 313.
Robert W. Stone & John W. Henry (2003). Identifying and Developing Measures of Information Technology Ethical Work Climates. Journal of Business Ethics 46 (4):337 - 350.
John R. Vokey (1998). Statistics Without Probability: Significance Testing as Typicality and Exchangeability in Data Analysis. Behavioral and Brain Sciences 21 (2):225-226.
Johannes Lenhard (2006). Models and Statistical Inference: The Controversy Between Fisher and Neyman–Pearson. British Journal for the Philosophy of Science 57 (1):69-91.
Stanley A. Mulaik (1985). Exploratory Statistics and Empiricism. Philosophy of Science 52 (3):410-430.
Kent Johnson (2011). Quantitative Realizations of Philosophy of Science: William Whewell and Statistical Methods. Studies in History and Philosophy of Science Part A 42 (3):399-409.
Added to index2009-01-28
Total downloads18 ( #94,242 of 1,102,744 )
Recent downloads (6 months)2 ( #182,643 of 1,102,744 )
How can I increase my downloads?