Calling time on 'statistical significance' in science research
Scientists should stop using the term 'statistically significant' in their research, urges this editorial in a special issue of The American Statistician published today.
The issue, Statistical Inference in the 21st Century: A World Beyond P<0.05, calls for an end to the practice of using a probability value (p-value) of less than 0.05 as strong evidence against a null hypothesis or a value greater than 0.05 as strong evidence favoring a null hypothesis. Instead, p-values should be reported as continuous quantities and described in language stating what the value means in the scientific context.
Containing 43 papers by statisticians from around the world, the special issue is expected to lead to a major rethinking of statistical inference by initiating a process that ultimately moves statistical science—and science itself—into a new age.
In the issue's editorial, Dr. Ronald Wasserstein, Executive Director of the ASA, Dr. Allen Schirm, retired from Mathematica Policy Research, and Professor Nicole Lazar of the University of Georgia said: "Based on our review of the articles in this special issue and the broader literature, we conclude that it is time to stop using the term 'statistically significant' entirely.
"No p-value can reveal the plausibility, presence, truth, or importance of an association or effect. Therefore, a label of statistical significance does not mean or imply that an association or effect is highly probable, real, true, or important. Nor does a label of statistical non-significance lead to the association or effect being improbable, absent, false, or unimportant.
"For the integrity of scientific publishing and research dissemination, therefore, whether a p-value passes any arbitrary threshold should not be considered at all when deciding which results to present or highlight."
Articles in the special issue suggest alternatives and complements to p-values, and highlight the need for widespread reform of editorial, educational and institutional practices.
While there is no single solution to replacing the outsized role that statistical significance has come to play in science, solid principles for the use of statistics do exist, say the editorial's authors.
"The statistical community has not yet converged on a simple paradigm for the use of statistical inference in scientific research—and in fact it may never do so," they acknowledge. "A one-size-fits-all approach to statistical inference is an inappropriate expectation. Instead, we recommend scientists conducting statistical analysis of their results should adopt what we call the ATOM model: Accept uncertainty, be Thoughtful, be Open, be Modest."