The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results*
Open accessAbstract
I follow R. A. Fisher's,The Design of Experiments (1935), using randomization statistical inference to test the null hypothesis of no treatment effects in a comprehensive sample of 53 experimental papers drawn from the journals of the American Economic Association. In the average paper, randomization tests of the significance of individual treatment effects find 13% to 22% fewer significant results than are found using authors’ methods. In joint tests of multiple treatment effects appearing together in tables, randomization tests yield 33% to 49% fewer statistically significant results than conventional tests. Bootstrap and jackknife methods support and confirm the randomization results.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
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(2021) · Wiley Series in Probability and Statistics · doi:10.1002/9781119243830.ch12 · Cited by 5,106
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Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Maximum Likelihood Estimation of Misspecified Models
White, H. (1982) · Econometrica · doi:10.2307/1912526 · Cited by 4,098
Maximum Likelihood Estimation of Misspecified Models
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“Several recent papers (Anderson 2008, Heckman et al 2010, Lee & Shaikh 2014, List, Shaikh & Xu 2016) have explored the robustness of individually significant results to stepdown randomization multiple-testing procedures in a few experiments.”
Background
Cited by
Understanding and misunderstanding randomized controlled trials
Deaton, A., & Cartwright, N. (2018) · Social Science & Medicine · doi:10.1016/j.socscimed.2017.12.005 · Cited by 1,841
Thinking, fast and slow? Some field experiments to reduce crime and dropout in Chicago
Heller, S. B., Shah, A. K., Guryan, J., Ludwig, J., Mullainathan, S., & Pollack, H. A. (2017) · The Quarterly Journal of Economics · doi:10.1093/qje/qjw033 · Cited by 493
“See, for example, Young (2015) for how the use of re-randomization tests can matter in practice.”
Cluster-robust inference: A guide to empirical practice
MacKinnon, J. G., Nielsen, M. Ø., & Webb, M. D. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.04.001 · Cited by 407
An Introductory Guide to Event Study Models
Miller, D. L. (2023) · Journal of Economic Perspectives · doi:10.1257/jep.37.2.203 · Cited by 360
Long-Run Effects of Lottery Wealth on Psychological Well-Being
Lindqvist, E., Östling, R., & Cesarini, D. (2020) · The Review of Economic Studies · doi:10.1093/restud/rdaa006 · Cited by 166
Metadata
- Authors
- Young, A.
- Venue
- The Quarterly Journal of Economics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-11-21
- Volume
- 134
- Issue
- 2
- Pages
- 557-598
- ISSN
- 0033-5533 (ISSN-L)
- 1531-4650
- OpenAlex
- W2900816330
- Cited by
- 631
- References
- 32
- FWCI
- 48.78
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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