Large sample size bias in empirical finance
Author(s)
Michaelides, Michael
Date Issued
October 31, 2020
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.
Journal
Finance Research Letters
Department
Economics
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
Publisher
Elsevier
Version of Article
Final manuscript post peer review, without publisher's formatting or copy
editing (postprint)
Embargo
This version of the article is available for viewing to the public after October 31, 2022.
DOI
10.1016/j.frl.2020.101835
ISSN
1544-6123
Rights
CC BY-NC-ND
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2020-10-31_Michaelides_Large.pdf
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Postprint
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