Article by Davidson Heath: “A century ago, two oddly domestic puzzles helped set the rules for what modern science treats as “real”: a Guinness brewer charged with quality control and a British lady insisting she can taste whether milk or tea was poured first.
Those stories sound quaint, but the machinery they inspired now decides which findings get published, promoted, and believed—and which get waved away as “not significant.” Instead of recognizing the limitations of statistical significance, fields including economics and medicine ossified around it, with dire consequences for science. In the 21st century, an obsession with statistical significance led to overprescription of both antidepressant drugs and a headache remedy with lethal side effects. There was another path we could have taken.
Sir Ronald Fisher succeeded 100 years ago in making statistical significance central to scientific investigation. Some scientists have argued for decades that blindly following his approach has led the scientific method down the wrong path. Today, statistical significance has brought many branches of science to a crisis of false-positive findings and bias.
At the beginning of the 20th century, the young science of statistics was blooming. One of the key innovations at this time was small-sample statistics—a toolkit for working with data that contain only a small number of observations. That method was championed by the great data scientist William S. Gosset. His ideas were largely ignored in favor of Fisher’s, and our ability to reach accurate and useful conclusions from data was harmed. It’s time to revive Gosset’s approach to experimentation and estimation…(More)”.
