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Theory & Psychology, Vol. 18, No. 1, 69-88 (2008)
DOI: 10.1177/0959354307086923

Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing

Raymond Hubbard

DRAKE UNIVERSITY, Raymond.Hubbard{at}drake.edu

R. Murray Lindsay

UNIVERSITY OF LETHBRIDGE, m.lindsay{at}uleth.ca

Reporting p values from statistical significance tests is common in psychology's empirical literature. Sir Ronald Fisher saw the p value as playing a useful role in knowledge development by acting as an `objective' measure of inductive evidence against the null hypothesis. We review several reasons why the p value is an unobjective and inadequate measure of evidence when statistically testing hypotheses. A common theme throughout many of these reasons is that p values exaggerate the evidence against H0. This, in turn, calls into question the validity of much published work based on comparatively small, including .05, p values. Indeed, if researchers were fully informed about the limitations of the p value as a measure of evidence, this inferential index could not possibly enjoy its ongoing ubiquity. Replication with extension research focusing on sample statistics, effect sizes, and their confidence intervals is a better vehicle for reliable knowledge development than using p values. Fisher would also have agreed with the need for replication research.

Key Words: likelihood ratios • null hypothesis • (overlapping) confidence intervals • p values • posterior probabilities • replication


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