An important lesson from this element of his discussion is that the statistical evidence against a null hypothesis is usually weaker when the data are interpreted by using the Bayes factor than when the same data are interpreted by using the P value approach. Few have done so, probably both because frequentist methods are universally taught, enshrined in statistical software, and expected by biomedical journals and because researchers are generally not familiar with alternative methods. Researchers will be particularly interested in Goodman's essay, therefore, because Bayesian principles can contribute importantly to the design of biomedical studies. But others stand to benefit as well from working their way through his analysis. This includes clinicians, who are increasingly required to interpret the strength of evidence from individual studies in making decisions at the bedside, and medical reporters, who are quick to seize on the latest individual trial without considering other available studies, thereby creating a great deal of unnecessary confusion.
Frequentist statistics can serve a useful purpose, but their limitations are many and serious.
Some members of the biostatistical community have therefore worked long and hard to encourage the medical researchers and readers to use the Bayesian approach to statistical inference in the design and interpretation of their studies. Goodman's article is an elegant reflection of those efforts, providing both an explication of underlying theory and solid suggestions for practice. In our view, this article will contribute importantly to the task of standing statistical inference right side up. We recommend it to our readers' most serious attention. Toward evidence-based medical statistics.