, Volume 180, Issue 1, pp 65–76

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

Open Access

DOI: 10.1007/s11229-009-9567-z

Cite this article as:
Sprenger, J. Synthese (2011) 180: 65. doi:10.1007/s11229-009-9567-z


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.


Models Data Inductive inference Nonparametric statistics Bootstrap resampling 

Copyright information

© The Author(s) 2009

Authors and Affiliations

  1. 1.Tilburg Center for Logic and Philosophy of ScienceTilburg UniversityTilburgThe Netherlands

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