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takyon writes:

Statistician Valen Johnson and 71 other researchers have proposed a redefinition of statistical significance in order to cut down on irreproducible results, especially those in the biomedical sciences. They propose "to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005" in a preprint article that will be published in an upcoming issue of Nature Human Behavior:

A megateam of reproducibility-minded scientists is renewing a controversial proposal to raise the standard for statistical significance in research studies. They want researchers to dump the long-standing use of a probability value (p-value) of less than 0.05 as the gold standard for significant results, and replace it with the much stiffer p-value threshold of 0.005.

Backers of the change, which has been floated before, say it could dramatically reduce the reporting of false-positive results—studies that claim to find an effect when there is none—and so make more studies reproducible. And they note that researchers in some fields, including genome analysis, have already made a similar switch with beneficial results.

"If we're going to be in a world where the research community expects some strict cutoff ... it's better that that threshold be .005 than .05. That's an improvement over the status quo," says behavioral economist Daniel Benjamin of the University of Southern California in Los Angeles, first author on the new paper, which was posted 22 July as a preprint article [open, DOI: 10.17605/OSF.IO/MKY9J] [DX] on PsyArXiv and is slated for an upcoming issue of Nature Human Behavior. "It seemed like this was something that was doable and easy, and had worked in other fields."

But other scientists reject the idea of any absolute threshold for significance. And some biomedical researchers worry the approach could needlessly drive up the costs of drug trials. "I can't be very enthusiastic about it," says biostatistician Stephen Senn of the Luxembourg Institute of Health in Strassen. "I don't think they've really worked out the practical implications of what they're talking about."

They have proposed a P-value of 0.005 because it corresponds to Bayes factors between approximately 14 and 26 in favor of H₁ (the alternative hypothesis), indicating "substantial" to "strong" evidence, and because it would reduce the false positive rate to levels they have judged to be reasonable "in many fields".

Is this good enough? Is it a good start?

OSF project page. If you have trouble downloading the PDF, use this link.

Original Submission