Effect sizes and p values: What should be reported and what should be replicated?

The most-criticized flaws of NHT can be avoided when the importance of a hypothesis is used to determine that a finding is worthy of report, and when p approximately equal to .05 is treated as insufficient basis for confidence in the replicability of an isolated non-null finding.

Abstract

Despite publication of many well-argued critiques of null hypothesis testing (NHT), behavioral science researchers continue to rely heavily on this set of practices. Although we agree with most critics' catalogs of NHT's flaws, this article also takes the unusual stance of identifying virtues that may explain why NHT continues to be so extensively used. These virtues include providing results in the form of a dichotomous (yes/no) hypothesis evaluation and providing an index (p value) that has a justifiable mapping onto confidence in repeatability of a null hypothesis rejection. The most-criticized flaws of NHT can be avoided when the importance of a hypothesis, rather than the p value of its test, is used to determine that a finding is worthy of report, and when p approximately equal to .05 is treated as insufficient basis for confidence in the replicability of an isolated non-null finding. Together with many recent critics of NHT, we also urge reporting of important hypothesis tests in enough descriptive detail to permit secondary uses such as meta-analysis.