Significance Testing in Empirical Finance: A Critical Review and Assessment
38 Pages Posted: 18 Mar 2014 Last revised: 29 Jan 2017
Date Written: June 8, 2015
This paper critically reviews the practice of significance testing in modern finance research. Employing a survey of recently published articles in four top-tier finance journals, we find that the conventional significance levels are exclusively used with little consideration of the key factors such as the sample size, power of the test, and expected losses. We also find that statistically significant results reported in many surveyed papers become questionable, if Bayesian method or revised standards for evidence were instead used. We observe strong evidence of publication bias in favour of statistical significance. We propose that substantial changes be made to the current practice of significance testing in finance research, in order to improve research credibility and integrity.
Keywords: Level of significance, Lindley paradox, Massive sample size, Meehl’s conjecture, Publication bias, Spurious statistical significance
JEL Classification: C12, G12
Suggested Citation: Suggested Citation