No p-value can reveal the plausibility, presence, truth, or importance of an association or effect

(written by lawrence krubner, however indented passages are often quotes). You can contact lawrence at:

I don’t understand why this started, 100 years ago, why it lasted as long as it did, or why right now it is coming to an end. I’m pleased to see this nonsense end, but I’m very curious why it happened at all. I suppose this was an expression of some aspect of the class war during the 20th Century, the rising importance of a certain group of professionals?

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 [quotes below].