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The Discovery of Argon: A Case for Learning from Data?

Published online by Cambridge University Press:  01 January 2022

Abstract

Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error-statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists't resourceful ways and strategies for detecting and eliminating error, as well as dealing with Duhemian ambiguities and underdetermination problems as they arose in the context of their local research settings.

Type
Research Article
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

I would like to thank Deborah Mayo and Alan Chalmers for many valuable comments and suggestions that improved the article considerably. Thanks are also due to two anonymous referees for many constructive comments and suggestions, especially on the history of this episode. Special thanks to my daughter Marina for bringing this historical episode to my attention.

References

Brock, W. H. 1992. The Norton History of Chemistry. New York: Norton.Google Scholar
Chalmers, A. F. 1999. What Is This Thing Called Science? 3rd ed. Indianapolis: Hackett.Google Scholar
Cox, D. R., and Hinkley, D. V. 1974. Theoretical Statistics. London: Chapman & Hall.CrossRefGoogle Scholar
Fisher, R. A. 1922. “On the Mathematical Foundations of Theoretical Statistics.” Philosophical Transactions of the Royal Society A 222:309–68.Google Scholar
Fisher, R. A., and Mackenzie, W. A. 1923. “Studies in Crop Variation.” Pt. 2, “The Manurial Response of Different Potato Varieties.” Journal of Agricultural Science 13:311–20.Google Scholar
Giunta, C. J. 1998. “Using History to Teach Scientific Method: The Case of Argon.” Journal of Chemical Education 75:1322–25.CrossRefGoogle Scholar
Giunta, C. J. 2001. “Argon and the Periodic System: The Piece That Would Not Fit.” Foundations of Chemistry 3:105–28.CrossRefGoogle Scholar
Gordin, M. D. 2004. A Well-Ordered Thing: Dmitri. Vol. 1, Mendeleev and the Shadow of the Periodic Table. New York: Basic.Google Scholar
Hacking, I. 1983. Representing and Intervening. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Harris, D. C. 2003. Quantitative Chemical Analysis. 6th ed. New York: Freeman.Google Scholar
Howson, C., and Urbach, P. 2005. Scientific Reasoning: The Bayesian Approach. 3rd. ed. Chicago: Open Court.Google Scholar
Kuhn, T. 1970. The Structure of Scientific Revolutions. 2nd ed. Chicago: University of Chicago Press.Google Scholar
Larsen, R. D. 1990. “Lessons Learned from Lord Rayleigh on the Importance of Data Analysis.” Journal of Chemical Education 67:925–28.CrossRefGoogle Scholar
Mayo, D. G. 1996. Error and the Growth of Experimental Knowledge. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Mayo, D. G. 2010. “Learning from Error, Severe Testing, and the Growth of Theoretical Knowledge.” In Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, ed. Mayo, D. G. and Spanos, A. 2010, 2857. Cambridge: Cambridge University Press.Google Scholar
Mayo, D. G., and Spanos, A. 2004. “Methodology in Practice: Statistical Misspecification Testing.” Philosophy of Science 71:1007–25.CrossRefGoogle Scholar
Mayo, D. G., and Spanos, A. 2006. “Severe Testing as a Basic Concept in a Neyman-Pearson Philosophy of Induction.” British Journal for the Philosophy of Science 57:323–57.CrossRefGoogle Scholar
Ramsay, W. 1899. “Note on the Densities of ‘Atmospheric Nitrogen,’ Pure Nitrogen, and Argon.” Proceedings of the Royal Society of London 64:181–83.Google Scholar
Ramsay, W. 1915. The Gases of the Atmosphere. 4th ed. London: Macmillan.Google Scholar
Rayleigh, Lord. 1893. “On the Densities of the Principal Gasses.” Proceedings of the Royal Society of London 53:134–49.Google Scholar
Rayleigh, Lord. 1894. “On an Anomaly Encountered in Determination of the Density of Nitrogen Gas.” Proceedings of the Royal Society of London 55:340–44.Google Scholar
Rayleigh, Lord. 1895. “Argon.” Science, n.s., 1:701–12.Google Scholar
Rayleigh, Lord, and Ramsay, William. 1895. “Argon, a New Constituent of the Atmosphere.” Philosophical Transactions of the Royal Society of London A 186:187241.Google Scholar
Scerri, E. R. 2007. The Periodic Table: Its Story and Its Significance. Oxford: Oxford University Press.Google Scholar
Skyrms, B. 2000. Choice and Chance: An Introduction to Inductive Logic. 4th ed. Stamford, CT: Wadsworth.Google Scholar
Spanos, A. 1999. Probability Theory and Statistical Inference. Cambridge: Cambridge University Press.Google Scholar
Spanos, A. 2007. “Curve-Fitting, the Reliability of Inductive Inference and the Error-Statistical Approach.” Philosophy of Science 74:1046–66.CrossRefGoogle Scholar
Staley, K. W. 2004. The Evidence for the Top Quark: Objectivity and Bias in Collaborative Experimentation. Cambridge: Cambridge University Press.Google Scholar
Stigler, S. M. 1986. The History of Statistics: The Measurement of Uncertainty before 1900. Cambridge, MA: Harvard University Press.Google Scholar
Student. 1908. “The Probable Error of the Mean.” Biometrika 6:125.CrossRefGoogle Scholar
Welch, B. L. 1938. “The Significance of the Difference between Two Means When the Population Variances Are Unequal.” Biometrika 29:350–62.CrossRefGoogle Scholar
Wolfenden, J. H. 1969. “The Noble Gases and the Periodic Table: Telling It Like It Was.” Journal of Chemical Education 46:569–76.CrossRefGoogle Scholar