Science without (parametric) models: the case of bootstrap resampling

Abstract

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.

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Acknowledgements

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.

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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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Sprenger, J. Science without (parametric) models: the case of bootstrap resampling. Synthese 180, 65–76 (2011). https://doi.org/10.1007/s11229-009-9567-z

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Keywords

  • Models
  • Data
  • Inductive inference
  • Nonparametric statistics
  • Bootstrap resampling