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AnonTechie writes:

In science, the success of an experiment is often determined by a measure called "statistical significance." A result is considered to be "significant" if the difference observed in the experiment between groups (of people, plants, animals and so on) would be very unlikely if no difference actually exists. The common cutoff for "very unlikely" is that you'd see a difference as big or bigger only 5 percent of the time if it wasn't really there — a cutoff that might seem, at first blush, very strict.

It sounds esoteric, but statistical significance has been used to draw a bright line between experimental success and failure. Achieving an experimental result with statistical significance often determines if a scientist's paper gets published or if further research gets funded. That makes the measure far too important in deciding research priorities, statisticians say, and so it's time to throw it in the trash.

More than 800 statisticians and scientists are calling for an end to judging studies by statistical significance in a March 20 comment published in Nature. An accompanying March 20 special issue of the American Statistician makes the manifesto crystal clear in its introduction: "'statistically significant' — don't say it and don't use it."

There is good reason to want to scrap statistical significance. But with so much research now built around the concept, it's unclear how — or with what other measures — the scientific community could replace it. The American Statistician offers a full 43 articles exploring what scientific life might look like without this measure in the mix.

Statistical Significance

Is is time for "P is less than or equal to 0.05" to be abandoned or changed ??

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