Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-22T05:55:45.864Z Has data issue: false hasContentIssue false

Loose Talk Kills: What’s Worrying about Unity of Method

Published online by Cambridge University Press:  01 January 2022

Abstract

There is danger in stressing commonalities among methods because the differences matter in fixing the meaning of our claims. Different methods can, and often do, test the same claim. But it takes a strong network of theory and empirical results to ensure that. Failing that, we are likely to fall into inference by pun. We use one set of methods to establish a claim and then draw inferences licensed by a similar-sounding claim that calls for different methods of testing. Our inferences fail, and bridges we build (or policies we set) depending on them fall down.

Type
Scientific Method Revisited
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Thanks to Alex Marcellesi for help with both ideas and production.

References

Cartwright, Nancy. 2007. Hunting Causes and Using Them. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Cartwright, Nancy, and Bradburn, Norm. 2011. “A Theory of Measurement.” In The Importance of Common Metrics for Advancing Social Science Theory and Research: Proceedings of the National Research Council Committee on Common Metrics, 5370. Washington, DC: National Research Council.Google Scholar
Cartwright, Nancy, Cat, Jordi, Fleck, Lola, and Uebel, Thomas E.. 1996. Otto Neurath: Philosophy between Science and Politics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Cartwright, Nancy, and Hardie, Jeremy. 2012. Evidence-Based Policy: A Practical Guide to Doing it Better. Oxford: Oxford University Press.CrossRefGoogle Scholar
Duflo, Esther, and Kremer, Michael. 2005. “Use of Randomization in the Evaluation of Development Effectiveness.” In Evaluating Development Effectiveness, ed. Pitman, George, Feinstein, Osvaldo, and Ingram, Gregory, 205–32. New Brunswick, NJ: Transaction.Google Scholar
Efstathiou, Sophia. 2009. “The Use of ‘Race’ as a Variable in Biomedical Research.” PhD diss., University of California, San Diego.Google Scholar
Galison, Peter. 1997. Image and Logic: A Material Culture of Microphysics. Chicago: University of Chicago Press.Google Scholar
Holland, Paul. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60.Google Scholar
Kuhn, Thomas. 1962. The Structure of Scientific Revolutions. Chicago: University of Chicago Press.Google Scholar
Meek, Christopher, and Glymour, Clark. 1994. “Conditioning and Intervening.” British Journal for the Philosophy of Science 45 (4): 1001–21.CrossRefGoogle Scholar
Pearl, Judea. 2000. Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.Google Scholar
Spirtes, Peter, Glymour, Clark, and Scheines, Richard. 2000. Causation, Prediction, and Search. Cambridge, MA: MIT Press.Google Scholar
Spohn, Wolfgang. 2001. “Bayesian Nets Are All There Is to Causal Dependence.” In Stochastic Causality, ed. Costantini, David, Galavotti, Maria Carla, and Suppes, Patrick. Stanford, CA: CSLI.Google Scholar
Wimsatt, William C. 2007. Piecewise Approximations to Reality: Engineering a Philosophy of Science for Limited Beings. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Woodward, James. 2003. Making Things Happen. Oxford: Oxford University Press.Google Scholar