After 4 decades of severe criticism, the ritual of null hypothesis significance testing—mechanical dichotomous decisions around a sacred .05 criterion—still persists. This article reviews the problems with this practice, including its near-universal misinterpretation of p as the probability that H ₀ is false, the misinterpretation that its complement is the probability of successful replication, and the mistaken assumption that if one rejects H ₀ one thereby affirms the theory that led to the test. Exploratory data analysis and the use of graphic methods, a steady improvement in and a movement toward standardization in measurement, an emphasis on estimating effect sizes using confidence intervals, and the informed use of available statistical methods is suggested. For generalization, psychologists must finally rely, as has been done in all the older sciences, on replication.

I make no pretense of the originality of my remarks in this article. One of the few things we, as psychologists, have learned from over a century of scientific study is that at age three score and 10, originality is not to be expected. David Bakan said back in 1966 that his claim that “a great deal of mischief has been associated” with the test of significance “is hardly original,” that it is “what ‘everybody knows,’” and that “to say it ‘out loud’ is … to assume the role of the child who pointed out that the emperor was really outfitted in his underwear” (p. 423). If it was hardly original in 1966, it can hardly be original now. Yet this naked emperor has been shamelessly running around for a long time.

Like many men my age, I mostly grouse. My harangue today is on testing for statistical significance, about which Bill Rozeboom (1960) wrote 33 years ago, “The statistical folkways of a more primitive past continue to dominate the local scene” (p. 417).

And today, they continue to continue. And we, as teachers, consultants, authors, and otherwise perpetrators of quantitative methods, are responsible for the ritualization of null hypothesis significance testing (NHST; I resisted the temptation to call it statistical hypothesis inference testing) to...