European Journal for Philosophy of Science

, Volume 2, Issue 1, pp 21–43 | Cite as

Strategies for securing evidence through model criticism

Original Paper in Philosophy of Science


Some accounts of evidence regard it as an objective relationship holding between data and hypotheses, perhaps mediated by a testing procedure. Mayo’s error-statistical theory of evidence is an example of such an approach. Such a view leaves open the question of when an epistemic agent is justified in drawing an inference from such data to a hypothesis. Using Mayo’s account as an illustration, I propose a framework for addressing the justification question via a relativized notion, which I designate security, meant to conceptualize practices aimed at the justification of inferences from evidence. I then show how the notion of security can be put to use by showing how two quite different theoretical approaches to model criticism in statistics can both be viewed as strategies for securing claims about statistical evidence.


Evidence Statistics Robustness Mis-specification testing Error-statistics Justification Security Statistical models 


  1. Achinstein, P. (2001). The book of evidence. New York: Oxford University Press.CrossRefGoogle Scholar
  2. Box, G. E. P., & Tiao, G. C. (1973). Bayesian inference in statistical analysis. Reading, Mass.: Addison-Wesley.Google Scholar
  3. Chalmers, D. (2011). The nature of epistemic space. In A. Egan, & B. Weatherson (Eds.), Epistemic modality. Oxford: Oxford University Press.Google Scholar
  4. Cox, D. R. (2006). Principles of statistical inference. New York: Cambridge University Press.CrossRefGoogle Scholar
  5. DeRose, K. (1991). Epistemic possibilities. The Philosophical Review, 100, 581–605.CrossRefGoogle Scholar
  6. Fisher, R. A. (1949). The design of experiments (5th edn). New York: Hafner Publishing Co.Google Scholar
  7. Hampel, F. (1968). Contributions to the theory of robust estimation. PhD thesis, University of California, Berkeley.Google Scholar
  8. Hampel, F. (1971). A general qualitative definition of robustness. The Annals of Mathematical Statistics, 42, 1887–1896.CrossRefGoogle Scholar
  9. Hampel, F. (1974). The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69, 383–393.CrossRefGoogle Scholar
  10. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust statistics: The approach based on influence functions. New York: John Wiley and Sons.Google Scholar
  11. Hintikka, J. (1962). Knowledge and belief: An introduction to the logic of the two notions. Ithaca: Cornell University Press.Google Scholar
  12. Huber, P. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35, 73–101.CrossRefGoogle Scholar
  13. Huber, P. (1981). Robust statistics. New York: John Wiley and Sons.CrossRefGoogle Scholar
  14. Kratzer, A. (1977). What ‘must’ and ‘can’ must and can mean. Linguistics and Philosophy, 1, 337–355.CrossRefGoogle Scholar
  15. MacFarlane, J. (2011). Epistemic modals are assessment-sensitive. In A. Egan, & B. Weatherson (Eds.), Epistemic modality. Oxford: Oxford University Press.Google Scholar
  16. Magnus, J. R. (2007). Local sensitivity in econometrics. In M. Boumans (Ed.), Measurement in economics: A handbook (pp. 295–319). Oxford: Academic.Google Scholar
  17. Magnus, J. R., & Vasnev, A. L. (2007). Local sensitivity and diagnostic tests. Econometrics Journal, 10, 166–192.CrossRefGoogle Scholar
  18. Mayo, D. G. (1992). Did Pearson reject the Neyman–Pearson philosophy of statistics? Synthese, 90, 233–262.CrossRefGoogle Scholar
  19. Mayo, D. G. (1996). Error and the growth of experimental knowledge. Chicago: University of Chicago Press.Google Scholar
  20. Mayo, D. G., & Spanos, A. (2004). Methodology in practice: Statistical misspecification testing. Philosophy of Science, 71, 1007–1025.CrossRefGoogle Scholar
  21. Mayo, D. G., & Spanos, A. (2006). Severe testing as a basic concept in a Neyman–Pearson philosophy of induction. The British Journal for the Philosophy of Science, 57(2), 323–357.CrossRefGoogle Scholar
  22. Neyman, J. (1950). First course in probability and statistics. New York: Henry Holt.Google Scholar
  23. Neyman, J. (1955). The problem of inductive inference. Communications on Pure and Applied Mathematics, VIII, 13–46.CrossRefGoogle Scholar
  24. Pearson, E. S. (1962). Some thoughts on statistical inference. Annals of Mathematical Statistics, 33, 394–403.CrossRefGoogle Scholar
  25. Spanos, A. (1999). Probability theory and statistical inference. Cambridge: Cambridge University Press.Google Scholar
  26. Sprenger, J. (2009). Science without (parametric) models: The case of bootstrap resampling. Synthese, published online. doi:10.1007/s11229-009-9567-z.Google Scholar
  27. Staley, K. (2008). Error-statistical elimination of alternative hypotheses. Synthese, 163, 397–408.CrossRefGoogle Scholar
  28. Staley, K., & Cobb, A. (2010). Internalist and externalist aspects of justification in scientific inquiry. Synthese, published online. doi:10.1007/s11229-010-9754-y.Google Scholar
  29. Stigler, S. (1973). Simon Newcomb, Percy Daniell, and the history of robust estimation 1885–1920. Journal of the American Statistical Association, 68, 872–879.CrossRefGoogle Scholar
  30. Tukey, J. (1960). A survey of sampling from contaminated distributions. In I. Olkin (Ed.), Contributions to probability and statistics: Essays in honor of Harold Hotelling (pp. 448–85). Stanford: Stanford University Press.Google Scholar

Copyright information

© Springer Science + Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of PhilosopySaint Louis UniversitySt. LouisUSA

Personalised recommendations