'Statistical significance' reaches its limits

Scientists are re-assessing one of their own most fundamental measurements: the use of statistical significance in research findings (as well as funding). An editorial co-authored by UGA statistics professor Nicole Lazar and published this week in a special issue of The American Statistician urges scientists to stop using the term:

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.

Extraordinary findings that are receiving a great deal of attention this week. Such a change would be a monumental shift in the utilization of p-value, a move to embrace uncertainty. The authors recommend that in place of a "one-size-fits-all" p-values the scientific community should adopt the ATOM model.

The acronyms stands for "Accept uncertainty, be Thoughtful, be Open, be Modest."