In scientific literature, 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.
More than 800 statisticians and scientists are calling for an end to judging studies by statistical significance. There is good reason to want to scrap statistical significance.
However, Many scientific studies today are designed around a framework of “null hypothesis significance testing” and with so much research now built around the concept of statistical significance, it’s unclear how — or with what other measures — the scientific community could replace it.
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