Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-10T14:02:03.554Z Has data issue: false hasContentIssue false
  • English
  • Français

Mismatch between scientific theories and statistical models

Published online by Cambridge University Press:  10 February 2022

Andrew Gelman Open the ORCID record for Andrew Gelman [Opens in a new window]
Andrew Gelman*
Affiliation:
Department of Statistics, Columbia University, New York, NY10027, USA. gelman@stat.columbia.edu; http://www.stat.columbia.edu/~gelman/
Get access Rights & Permissions [Opens in a new window]

Abstract

Yarkoni recommends that psychology researchers should take care to align their statistical models to the verbal theories they are studying and testing. This principle applies not just to qualitative theories in psychology but also to more quantitative sciences: there, too, mismatch between open-ended theories and specific statistical models have led to confusion.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e15
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chen, Y., Ebenstein, A., Greenstone, M., & Li, H. (2013). Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policy. Proceedings of the National Academy of Sciences, 110, 12936–12941.CrossRefGoogle ScholarPubMed
Durante, K. M., Arsena, A. R., & Griskevicius, V. (2013). The fluctuating female vote: Politics, religion, and the ovulatory cycle. Psychological Science, 24, 1007–1016.CrossRefGoogle ScholarPubMed
Gelman, A. (2015). The connection between varying treatment effects and the crisis of unreplicable research: A Bayesian perspective. Journal of Management, 41, 632–643.CrossRefGoogle Scholar
Gelman, A., & Imbens, G. (2019). Why high-order polynomials should not be used in regression discontinuity designs. Journal of Business and Economic Statistics, 37, 447–456.CrossRefGoogle Scholar
Gelman, A., & Zelizer, A. (2015). Evidence on the deleterious impact of sustained use of polynomial regression on causal inference. Research and Politics, 2, 1–7.CrossRefGoogle Scholar
Lakatos, I. (1978). Philosophical papers. Cambridge University Press.Google Scholar
Mayo, D. G. (1996). Error and the growth of experimental knowledge. University of Chicago Press.CrossRefGoogle Scholar
Popper, K. R. (1934/1959). The logic of scientific discovery. London: Hutchinson.Google Scholar