David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Philosophy of Science 62 (3):438-458 (1995)
I respond to H. M. Collins's claim (1985, 1990, 1993) that experimental inquiry cannot be objective because the only criterium experimentalists have for determining whether a technique is "working" is the production of "correct" (i.e., the expected) data. Collins claims that the "experimenters' regress," the name he gives to this data-technique circle, cannot be broken using the resources of experiment alone. I argue that the data-technique circle, can be broken even though any interpretation of the raw data produced by techniques is theory-dependent. However, it is possible to break this circle by eliminating dependence on even those theoretical presuppositions that are shared by an entire scientific community through the use of multiple independently theory-dependent techniques to produce robust bodies of data. Moreover, I argue, that it is the production of robust bodies of data that convinces experimentalists of the objectivity of their data interpretations
|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
Jonathan Y. Tsou (2012). Intervention, Causal Reasoning, and the Neurobiology of Mental Disorders: Pharmacological Drugs as Experimental Instruments. Studies in History and Philosophy of Science Part C 43 (2):542-551.
Anna Leuschner (2012). Pluralism and Objectivity: Exposing and Breaking a Circle. Studies in History and Philosophy of Science Part A 43 (1):191-198.
Uljana Feest (2011). What Exactly is Stabilized When Phenomena Are Stabilized? Synthese 182 (1):57-71.
François Claveau (2011). Evidential Variety as a Source of Credibility for Causal Inference: Beyond Sharp Designs and Structural Models. Journal of Economic Methodology 18 (3):233-253.
Similar books and articles
Andrew Ward & Pamela Jo Johnson (2008). Addressing Confounding Errors When Using Non-Experimental, Observational Data to Make Causal Claims. Synthese 163 (3):419 - 432.
Lewis L. H. Chung & Keith C. C. Chan (2003). Evolutionary Discovery of Fuzzy Concepts in Data. Brain and Mind 4 (2):253-268.
Joe Giffels (2010). Sharing Data is a Shared Responsibility. Science and Engineering Ethics 16 (4):801-803.
Emma Smith (2008). Pitfalls and Promises: The Use of Secondary Data Analysis in Educational Research. British Journal of Educational Studies 56 (3):323 - 339.
Clark A. Chinn & William F. Brewer (1996). Mental Models in Data Interpretation. Philosophy of Science 63 (3):219.
Donald L. Rowe & James Wright (2001). Using Experimental Data and Analysis in EEG Modelling. Behavioral and Brain Sciences 24 (5):828-829.
Jaakko Hintikka (2005). Omitting Data—Ethical or Strategic Problem? Synthese 145 (2):169 - 176.
Todd Harris (2003). Data Models and the Acquisition and Manipulation of Data. Philosophy of Science 70 (5):1508-1517.
Siu L. Chow (1995). In Defense of Experimental Data in a Relativistic Milieu. Philosophical Explorations.
Gregory Gandenberger (2010). Producing a Robust Body of Data with a Single Technique. Philosophy of Science 77 (3):381-399.
Added to index2009-01-28
Total downloads35 ( #92,066 of 1,725,560 )
Recent downloads (6 months)9 ( #72,348 of 1,725,560 )
How can I increase my downloads?