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Frequentist Model-based Statistical Induction and the Replication Crisis

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  • Aris Spanos

    (Virginia Tech)

Abstract

The prevailing view in the current replication crisis literature is that the non-replicability of published empirical studies (a) confirms their untrustworthiness, and (b) the primary source of that is the abuse of frequentist testing, in general, and the p-value in particular. The main objective of the paper is to challenge both of these claims and make a case that (a) non-replicability does not necessarily imply untrustworthiness and (b) the abuses of frequentist testing are only symptomatic of a much broader problem relating to the uninformed and recipe-like implementation of statistical modeling and inference that contributes significantly to untrustworthy evidence. It is argued that the crucial contributors to the untrustworthiness relate (directly or indirectly) to the inadequate understanding and implementation of the stipulations required for model-based statistical induction to give rise to trustworthy evidence. It is argued that these preconditions relate to securing reliable ‘learning from data’ about phenomena of interest and pertain to the nature, origin, and justification of genuine empirical knowledge, as opposed to beliefs, conjectures, and opinions.

Suggested Citation

  • Aris Spanos, 2022. "Frequentist Model-based Statistical Induction and the Replication Crisis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 133-159, September.
    • Handle: RePEc:spr:jqecon:v:20:y:2022:i:1:d:10.1007_s40953-022-00312-z
    DOI: 10.1007/s40953-022-00312-z
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    References listed on IDEAS

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    1. Sundberg,Rolf, 2019. "Statistical Modelling by Exponential Families," Cambridge Books, Cambridge University Press, number 9781108701112, October.
    2. Spanos,Aris, 2019. "Probability Theory and Statistical Inference," Cambridge Books, Cambridge University Press, number 9781316636374, September.
    3. Sundberg,Rolf, 2019. "Statistical Modelling by Exponential Families," Cambridge Books, Cambridge University Press, number 9781108476591, October.
    4. Spanos, Aris, 2010. "Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification," Journal of Econometrics, Elsevier, vol. 158(2), pages 204-220, October.
    5. Daniel J. Benjamin & James O. Berger & Magnus Johannesson & Brian A. Nosek & E.-J. Wagenmakers & Richard Berk & Kenneth A. Bollen & Björn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Chr, 2018. "Redefine statistical significance," Nature Human Behaviour, Nature, vol. 2(1), pages 6-10, January.
      • Daniel Benjamin & James Berger & Magnus Johannesson & Brian Nosek & E. Wagenmakers & Richard Berk & Kenneth Bollen & Bjorn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Christopher Chambe, 2017. "Redefine Statistical Significance," Artefactual Field Experiments 00612, The Field Experiments Website.
    6. Aris Spanos & Anya McGuirk, 2001. "The Model Specification Problem from a Probabilistic Reduction Perspective," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(5), pages 1168-1176.
    7. Aris Spanos, 2018. "Mis†Specification Testing In Retrospect," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 541-577, April.
    8. Aris Spanos, 2006. "Revisiting the omitted variables argument: Substantive vs. statistical adequacy," Journal of Economic Methodology, Taylor & Francis Journals, vol. 13(2), pages 179-218.
    9. Jeffrey T. Leek & Roger D. Peng, 2015. "Statistics: P values are just the tip of the iceberg," Nature, Nature, vol. 520(7549), pages 612-612, April.
    10. Spanos,Aris, 1986. "Statistical Foundations of Econometric Modelling," Cambridge Books, Cambridge University Press, number 9780521269124, October.
    11. Spanos, Aris, 1990. "The simultaneous-equations model revisited : Statistical adequacy and identification," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 87-105.
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