Addendum: see also this article in FiveThirtyEight: "Statisticians Found One Thing They Can Agree On: It’s Time To Stop Misusing P-Values."Most casual readers of scientific research know that for results to be declared “statistically significant,” they need to pass a simple test. The answer to this test is called a p-value. And if your p-value is less than .05 — bingo, you got yourself a statistically significant result.Now a group of 72 prominent statisticians, psychologists, economists, sociologists, political scientists, biomedical researchers, and others want to disrupt the status quo. A forthcoming paper in the journal Nature Human Behavior argues that results should only be deemed “statistically significant” if they pass a higher threshold.“We propose a change to P< 0.005,” the authors write. “This simple step would immediately improve the reproducibility of scientific research in many fields.”...The proposal has critics. One of them is Daniel Lakens, a psychologist at Eindhoven University of Technology in the Netherlands who is currently organizing a rebuttal paper with dozens of authors. Mainly, he says the significance proposal might work to stifle scientific progress.
How many statisticians does it take to ensure at least a 50 percent chance of a disagreement about p-values? According to a tongue-in-cheek assessment by statistician George Cobb of Mount Holyoke College, the answer is two … or one. So it’s no surprise that when the American Statistical Association gathered 26 experts to develop a consensus statement on statistical significance and p-values, the discussion quickly became heated.Continued at the link.
It may sound crazy to get indignant over a scientific term that few lay people have even heard of, but the consequences matter. The misuse of the p-value can drive bad science (there was no disagreement over that), and the consensus project was spurred by a growing worry that in some scientific fields, p-values have become a litmus test for deciding which studies are worthy of publication. As a result, research that produces p-values that surpass an arbitrary threshold are more likely to be published, while studies with greater or equal scientific importance may remain in the file drawer, unseen by the scientific community.
The results can be devastating...