| Sumario: | This paper argues that a Fisherian approach to statistical inference, which views statistical testing as determining the chance probability of an event, is coherent, consistent with modern statistical methods and forms a sound theoretical basis for the use of statistical tests in psychology. It is argued that Fisherian statistical tests are concerned with establishing the direction of tested effects, give rise to confidence intervals and are quite consistent with power analyses. Researchers are encouraged to report chance probabilities, and to interpret them according to the prevailing conditions rather than using a fixed decision rule. The contrasting Neyman-Pearson approach, which views statistical testing as a quality control procedure for accepting hypotheses, posits unreasonable research practices which psychologists do not and should not be expected to follow. Neyman-Pearson theory has caused confusion in the psychological literature and criticisms have been levelled at statistical testing in general that ought to have been directed specifically at Neyman-Pearson testing. Statistical inference, of any sort, is held to he insufficient to characterize the process of testing scientific hypotheses. Data should be seen as evidence to be used in psychological arguments and statistical significance is just one measure of its quality. It restrains researchers from making too much of findings which could otherwise be explained by chance. Reprinted by permission of the publisher.
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