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Robustness, Discordance, and Relevance

Published online by Cambridge University Press:  01 January 2022

Jacob Stegenga
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Abstract

Robustness is a common platitude: hypotheses are better supported with evidence generated by multiple techniques that rely on different background assumptions. Robustness has been put to numerous epistemic tasks, including the demarcation of artifacts from real entities, countering the “experimenter's regress,” and resolving evidential discordance. Despite the frequency of appeals to robustness, the notion itself has received scant critique. Arguments based on robustness can give incorrect conclusions. More worrying is that although robustness may be valuable in ideal evidential circumstances (i.e., when evidence is concordant), often when a variety of evidence is available from multiple techniques, the evidence is discordant.

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Type
Research Article
Information
Philosophy of Science , Volume 76 , Issue 5 , December 2009 , pp. 650 - 661
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

‡

Numerous people have given valuable commentary on this paper, including Nancy Cartwright, Craig Callender, Matthew Brown, Marta Halina, Kareem Khalifa, Eric Martin, Tarun Menon, Boaz Miller, Brendan Ritchie, Samuel Schindler, Léna Soler, Eran Tal, and audiences at the Canadian Society for the History and Philosophy of Science, the Philosophy of Science Association, and members of the University of California, San Diego Philosophy of Science Reading Group.

References

Achinstein, Peter (2001), The Book of Evidence. New York: Oxford University Press.CrossRefGoogle Scholar
Bechtel, William (2006), Discovering Cell Mechanisms. Cambridge: Cambridge University Press.Google Scholar
Box, George (1953), “Non-normality and Tests on Variances”, Non-normality and Tests on Variances 40:318–335.Google Scholar
Carnap, Rudolf (1950), Logical Foundations of Probability. Chicago: University of Chicago Press.Google Scholar
Cartwright, Nancy (1983), How the Laws of Physics Lie. Oxford: Clarendon.CrossRefGoogle Scholar
Cartwright, Nancy (2007), “Are RCTs the Gold Standard?”, Are RCTs the Gold Standard? 2:11–20.Google Scholar
Collins, Harry (1985), Changing Order: Replication and Induction in Scientific Practice. Chicago: University of Chicago Press.Google Scholar
Culp, Sylvia (1994), “Defending Robustness: The Bacterial Mesosome as a Test Case”, in Hull, David, Forbes, Micky, and Burian, Richard M. (eds.), PSA 1994: Proceedings of the 1994 Biennial Meeting of the Philosophy of Science Association, Vol. 1. East Lansing, MI: Philosophy of Science Association, 46–57.Google Scholar
Franklin, Allan (2002), Selectivity and Discord: Two Problems of Experiment. Pittsburgh: University of Pittsburgh PressGoogle Scholar
Franklin, Allan, and Howson, Colin (1984), “Why Do Scientists Prefer to Vary Their Experiments?”, Why Do Scientists Prefer to Vary Their Experiments? 15:51–62.Google Scholar
Galison, Peter (1987), How Experiments End. Chicago: University of Chicago Press.Google Scholar
Hacking, Ian (1983), Representing and Intervening. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Horwich, Paul (1982), Probability and Evidence. Cambridge: Cambridge University Press.Google Scholar
Howson, Colin, and Urbach, Peter (1989), Scientific Reasoning: The Bayesian Approach. LaSalle, IL: Open Court.Google Scholar
Hudson, Robert G. (1999), “Mesosomes: A Study in the Nature of Experimental Reasoning”, Mesosomes: A Study in the Nature of Experimental Reasoning 66 (2): 289–309..Google Scholar
Levins, Richard (1966), “The Strategy of Model Building in Population Biology”, The Strategy of Model Building in Population Biology 54:421–431.Google Scholar
Magnus, P. D., and Callender, Craig (2004), “Realist Ennui and Base Rates”, Realist Ennui and Base Rates 71 (3): 320–338..Google Scholar
Mayo, Deborah (1996), Error and the Growth of Experimental Knowledge. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Nye, Mary Jo (1972), Molecular Reality: A Perspective on the Scientific Work of Jean Perrin. London: Macdonald.Google Scholar
Rasmussen, Nicolas (1993), “Facts, Artifacts, and Mesosomes: Practicing Epistemology with the Electron Microscope”, Facts, Artifacts, and Mesosomes: Practicing Epistemology with the Electron Microscope 24 (2): 221–265..Google Scholar
Rasmussen, Nicolas (2001), “Evolving Scientific Epistemologies and the Artifacts of Empirical Philosophy of Science: A Reply Concerning Mesosomes”, Evolving Scientific Epistemologies and the Artifacts of Empirical Philosophy of Science: A Reply Concerning Mesosomes 16:629–654.Google Scholar
Salmon, Wesley (1984), Scientific Explanation and the Causal Structure of the World. Princeton, NJ: Princeton University Press.Google Scholar
Staley, Kent (2004), “Robust Evidence and Secure Evidence Claims”, Robust Evidence and Secure Evidence Claims 71:467–488.Google Scholar
Stegenga, J. (n.d.), “Multimodal Evidence”, unpublished manuscript.Google Scholar
U.K. Department of Health (2007), Pandemic Influenza: Guidance for Infection Control in Hospitals and Primary Care Settings. London: U.K. Department of Health.Google Scholar
Wimsatt, William (1981), “Robustness, Reliability, and Overdetermination in Science”, in Brewer, Marilyn B. and Collins, Barry E. (eds.), Scientific Inquiry and the Social Sciences: A Volume in Honor of Donald T. Campbell. San Francisco: Jossey-Bass, 124–163.Google Scholar
Worrall, John (2002), “What Evidence in Evidence-Based Medicine?”, What Evidence in Evidence-Based Medicine? 69 (Proceedings): S316–S330.Google Scholar