Science without (parametric) models: the case of bootstrap resampling
- 498 Downloads
Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make more parsimonious assumptions and often replace theoretical understanding and knowledge about mechanisms by careful experimental design. Thus, it is worthwhile to study in detail how nonparametric models serve as inferential engines in science.
KeywordsModels Data Inductive inference Nonparametric statistics Bootstrap resampling
I would like to thank the audience at the Models and Simulations 2 conference in Tilburg and the London-Paris-Tilburg Workshop in Philosophy of Science for their helpful criticisms and suggestions. In particular, I would like to thank Roman Frigg, Stephan Hartmann, Kevin Hoover, Jan-Willem Romeijn, Jonah Schupbach, Leonard Smith and Michael Weisberg as well as two anonymous referees of this journal for their detailed and stimulating feedback.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
- Burnham K.P., Anderson D.R. (1998) Model selection and inference: a practical information–theoretic approach. Springer, New YorkGoogle Scholar
- Cox D.R. (2006) Principles of statistical inference. Cambridge University Press, CambridgeGoogle Scholar
- Efron B., Tibshirani R. (1993) An introduction to the bootstrap. Chapman & Hall, LondonGoogle Scholar
- Glymour C. (1980) Theory and evidence. Princeton University Press, PrincetonGoogle Scholar
- Hacking I. (1965) Logic of statistical inference. Cambridge University Press, CambridgeGoogle Scholar
- Humphreys Paul (2004) Extending ourselves: Computational science, empiricism, and scientific method. Oxford University Press, OxfordGoogle Scholar
- Mayo D.G. (1996) Error and the growth of experimental knowledge. The University of Chicago Press, Chicago & LondonGoogle Scholar
- Royall R. (1997) Statistical evidence: A likelihood paradigm. Chapman & Hall, LondonGoogle Scholar
- Spirtes P., Glymour C., Scheines R. (1993) Causation, prediction, and search. Springer, New YorkGoogle Scholar
- Suppes, P. (1969). Models of data. In P. Suppes (Ed.), Studies in the methodology and foundations of science. Selected Papers from 1951 to 1969 (pp. 24–35). Dordrecht: Reidel. Orginally published in Ernest Nagel, P. Suppes & A. Tarski (Eds.), Logic, methodology and philosophy of science: proceedings of the 1960 international congress (pp. 252–261). Stanford: Stanford University Press, 1962.Google Scholar
- Weisberg, M. (2009). Models of modeling. Unpublished manscript source. http://www.phil.upenn.edu/~weisberg/Homepage/Papers.html