The fallacy of the null-hypothesis significance test.

The theory of probability and statistical inference is various things to various people. To the mathematician, it is an intricate formal calculus, to be explored and developed with little professional concern for any empirical significance that might attach to the terms and propositions involved. To the philosopher, it is an embarrassing mystery whose justification and conceptual clarification have remained stubbornly refractory to philosophical insight. (A famous philosophical epigram has it that induction [a special case of statistical inference] is the glory of science and the scandal of philosophy.) To the experimental scientist, however, statistical inference is a research instrument, a processing device by which unwieldy masses of raw data may be refined into a product more suitable for assimilation into the corpus of science, and in this lies both strength and weakness. It is strength in that, as an ultimate consumer of statistical methods, the experimentalist is in position to demand that the techniques made available to him conform to his actual needs. But it is also weakness in that, in his need for the tools constructed by a highly technical formal discipline, the experimentalist, who has specialized along other lines, seldom feels competent to extend criticisms or even comments; he is much more likely to make unquestioning application of procedures learned more or less by rote from persons assumed to be more knowledgeable of statistics than he. There is, of course, nothing surprising