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dc.contributor.authorMichaelides, Michael
dc.date.accessioned2021-10-08T20:36:23Z
dc.date.available2021-10-08T20:36:23Z
dc.date.issued2020-10-31
dc.identifier.citationMichaelides, M. (2021). Large sample size bias in empirical finance. Finance Research Letters, 41, 101835. doi:https://doi.org/10.1016/j.frl.2020.101835en_US
dc.identifier.issn1544-6123
dc.identifier.urihttps://dspace.allegheny.edu/handle/10456/53961
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofFinance Research Lettersen_US
dc.relation.isversionofhttps://www.sciencedirect.com/science/article/pii/S1544612320316494en_US
dc.rightsCC BY-NC-NDen_US
dc.subjectLarge sample sizeen_US
dc.subjectHigh statistical poweren_US
dc.subjectSpurious statistical significanceen_US
dc.subjectAppropriate significance thresholdsen_US
dc.subjectMethodological crisisen_US
dc.subjectPublication biasen_US
dc.titleLarge sample size bias in empirical financeen_US
dc.description.versionFinal manuscript post peer review, without publisher's formatting or copy editing (postprint)en_US
dc.contributor.departmentEconomicsen_US
dc.description.embargoThis version of the article is available for viewing to the public after October 31, 2022.en_US
dc.citation.volume31en_US
dc.identifier.doi10.1016/j.frl.2020.101835
dc.contributor.avlauthorMichaelides, Michael


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