Title stolen from article of same name by Leland Teschler in the trade journal Machine Design.
Update Statisticians often screw up statistics, too. See below.
The article is the result of an interview I gave Teschler a month ago. He called me up and asked about bad statistics, and I became that obnoxious guy in the bar who grabs your elbow and won’t let go until you understand his theory of life, the universe, and everything. Poor Teschler was panting by the time I finished with him.
Yet he must have recovered sufficiently to write:
Briggs’ argument for such a radical stance is that most nonexperts misapply these ideas and often use them to leap to bad conclusions. “The technical definition of a p-value is so difficult to remember that people just donâ€™t keep it in mind. Even the Wikipedia page on p-value has a couple of small errors,” Briggs says. “People treat a p-value as a magical thing: If you get a p-value less than a magic number then your hypothesis is true. People don’t actually say it is 100% true, but they behave as though it is.”…
“P-values can and are used to prove anything and everything. The sole limitation is the imagination of the researcher,” he says. “To the civilian, the small p-value says that statistical significance has been found, and this, in turn, says that his hypothesis is not just probable, but true.”
Why not eliminate frequentist statistics for all but math PhD students and teach Bayes or, my preference, logical probability?
Nevertheless, there is only a slim chance a Bayesian revolution will sweep through statistics classrooms. The problem is one of inertia. “Most statistics classes are taught by nonstatisticians. They can’t teach Bayesian statistics because a lot of them have never heard of it,” says Briggs. Even worse, “Peer-review journal editors still want to see p-values in the papers they publish.”
Update The interview I had with Teschler was wide ranging and did not focus on who was king of the statistical hill. I frankly do not care. The main complaint against me was that I am an academic. Ouch. I am so, it’s true, but only for two weeks of every year. The rest of the time I am on my own. Because why? Because the crazy ideas I espouse do not endear me to professional academics.
I didn’t appreciate that some people might take exception to the claim that professionals would be better at statistic than non-professionals. Of course, it is always possible that any non-trained person would do better than a trained one in statistics, or in any field.
My main point with Teschler was that statistics as a field was broken. Regular readers will understand just what I mean by this. Countless times I have showed that the further a field gets from the simple, the worse the evidence is handled. Most engineering is simple, and subject to much feedback, at least compared to the monstrous complexity which is human behavior.
If you’re new here, have a look around and you’ll see quickly what I mean.