David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Synthese 163 (3):305 - 314 (2008)
We argue for a naturalistic account for appraising scientific methods that carries non-trivial normative force. We develop our approach by comparison with Laudan’s (American Philosophical Quarterly 24:19–31, 1987, Philosophy of Science 57:20–33, 1990) “normative naturalism” based on correlating means (various scientific methods) with ends (e.g., reliability). We argue that such a meta-methodology based on means–ends correlations is unreliable and cannot achieve its normative goals. We suggest another approach for meta-methodology based on a conglomeration of tools and strategies (from statistical modeling, experimental design, and related fields) that affords forward looking procedures for learning from error and for controlling error. The resulting “error statistical” appraisal is empirical—methods are appraised by examining their capacities to control error. At the same time, this account is normative, in that the strategies that pass muster are claims about how actually to proceed in given contexts to reach reliable inferences from limited data.
|Keywords||Normative naturalism Reliable inference Methodology Meta-methodology Error statistics Learning from error Controlling error Appraising scientific methods Laudan Canonical models of error Means–ends approaches|
|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
Thomas S. Kuhn (1962). The Structure of Scientific Revolutions Vol. The University of Chicago Press.
Ronald N. Giere (2006). Scientific Perspectivism. University of Chicago Press.
Hilary Kornblith (2002). Knowledge and its Place in Nature. Oxford University Press.
Deborah G. Mayo (2001). Error and the Growth of Experimental Knowledge. International Studies in the Philosophy of Science 15 (1):455-459.
A. F. Chalmers (1999). What is This Thing Called Science? Monograph Collection (Matt - Pseudo).
Citations of this work BETA
No citations found.
Similar books and articles
Deborah G. Mayo (1983). An Objective Theory of Statistical Testing. Synthese 57 (3):297 - 340.
Wendy S. Parker (2008). Computer Simulation Through an Error-Statistical Lens. Synthese 163 (3):371 - 384.
Kent W. Staley, Strategies for Securing Evidence Through Model Criticism: An Error-Statistical Perspective.
Aris Spanos (2009). Error in Economics and the Error Statistical Approach Error in Economics. Towards a More Evidence-Based Methodology , Julian Reiss, Routledge, 2007, XXIV + 246 Pages. [REVIEW] Economics and Philosophy 25 (2):206-210.
Kent W. Staley (2005). Agency and Objectivity in the Search for the Top Qjjark. In P. Achinstein (ed.), Scientific Evidence: Philosophical Theories & Applications. The Johns Hopkins University Press
R. P. Farrell & C. A. Hooker (2009). Error, Error-Statistics and Self-Directed Anticipative Learning. Foundations of Science 14 (4):249-271.
Bart Streumer (2013). Can We Believe the Error Theory? Journal of Philosophy 110 (4):194-212.
David Wÿss Rudge (2001). Kettlewell From an Error Statisticians's Point of View. Perspectives on Science 9 (1):59-77.
Deborah G. Mayo (1997). Error Statistics and Learning From Error: Making a Virtue of Necessity. Philosophy of Science 64 (4):212.
Added to index2009-01-28
Total downloads34 ( #112,409 of 1,789,791 )
Recent downloads (6 months)3 ( #261,181 of 1,789,791 )
How can I increase my downloads?