Students at times come to my office to tell me that their professors in research methods courses emphasize the importance of null hypotheses and hypothesis testing. It always brings to mind the last words Gary Chamberlain said in his econometrics class: "I didn't mention hypothesis testing, because I don't like it. And don't ever use those damned stars." [I am, of course, paraphrasing, as I took the class 25 years ago].
In any event, I ran across this nice critique of hypothesis testing. The point of statistics, after all, should be parameter estimation and prediction, not some arbitrary test of statistical significance. In large samples, statistically significant parameters can be unimportant; in small samples, statistically insignificant parameters can simply mean that we don't really know what is going on.
Much of the confusion underlying how medical information is conveyed in the media arises from the hypothesis testing tradition: the media infer that if x is a statistically significant predictor of y, then x must be important in determining y. This is not necessarily so.
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