Uncovering the Iceberg from Its Tip: A Model of Publication Bias and p-Hacking

32 Pages Posted: 22 Jun 2021 Last revised: 30 Jun 2021

See all articles by Campbell R. Harvey

Campbell R. Harvey

Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER)

Yan Liu

Purdue University

Date Written: June 29, 2021

Abstract

Harvey, Liu, and Zhu (2016) argue that a large proportion of published asset-pricing factors are likely false. Researchers may try many variables and report only the significant ones, so-called p-hacking. Some recent work challenges the prevalence of p-hacking and argues that the amount of shrinkage necessary for reported results is trivial. We present a model where there are true anomalies and false anomalies. Our model does a good job of fitting the observed population. Our evidence is consistent with the idea that a large proportion of anomalies are false and reinforces the need to raise the thresholds for statistical significance.

Keywords: p-hacking, Data mining, Anomalies, Simulations, Publication bias, Multiple testing

JEL Classification: C12, G10, G12

Suggested Citation

Harvey, Campbell R. and Liu, Yan, Uncovering the Iceberg from Its Tip: A Model of Publication Bias and p-Hacking (June 29, 2021). Available at SSRN: https://ssrn.com/abstract=3865813 or http://dx.doi.org/10.2139/ssrn.3865813

Campbell R. Harvey (Contact Author)

Duke University - Fuqua School of Business ( email )

Box 90120
Durham, NC 27708-0120
United States
919-660-7768 (Phone)

HOME PAGE: http://www.duke.edu/~charvey

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Yan Liu

Purdue University ( email )

West Lafayette, IN 47907-1310
United States

HOME PAGE: http://yliu1.com

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