Large sample size bias in empirical finance
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Issue Date
2020-10-31
Authors
Michaelides, Michael
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This version of the article is available for viewing to the public after October 31, 2022.
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Keywords
Large sample size , High statistical power , Spurious statistical significance , Appropriate significance thresholds , Methodological crisis , Publication bias
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Abstract
The vast majority of empirical studies in finance employ large enough sample sizes and use the conventional thresholds for statistical significance. This routine practice can potentially lead to spurious statistically significant results. The primary aim of this paper is to present a rule of thumb that can be used to determine the appropriate thresholds for statistical significance for a given sample size. The paper argues that the list of statistically significant findings in the broader finance literature is likely to be much shorter after accounting for large sample size bias.
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Department
Economics
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License
CC BY-NC-ND
Citation
Michaelides, M. (2021). Large sample size bias in empirical finance. Finance Research Letters, 41, 101835. doi:https://doi.org/10.1016/j.frl.2020.101835
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Final manuscript post peer review, without publisher's formatting or copy
editing (postprint)
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Publisher
Elsevier