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The Nonutility of Significance Tests: The Significance of Tests of Significance Reconsidered

Nearly everyone in print supports the use of tests of significance under one condition or another. McGinnis (1958) and Kish (1959) advocate their application, whenever a random sampling procedure has been employed, for the purpose of either testing conditional or unconditional hypotheses (McGinnis) or to separate out the effects of explanatory variables and randomized variables (Kish). Consistent with this perspective, Morrison and Henkel (1969) maintain that tests of significance can be used only if a random sample has been taken and the population is concisely designated. Several others also support this usage, which can be considered the classical approach to the use of significance tests in survey research (Blalock, 1960; Siegel, 1956; Duggan and Dean, 1968; Skipper et al., 1967; Anderson and Zelditch, 1968; and Mueller and Schuessler, 1961). Even the more severe critics of significance tests stress their "legitimate use" or "utility," given rather specific conditions. Selvin (1957), although maintaining that they are not appropriate for sample survey data (because of "correlated biases"), advocates their applicability to experimental situations where randomization is used. With randomization, he argues, truly causal relations can be determined, and interpretations of relations are not hampered by the confounding effects of other variables. Camilleri (1962), another severe critic of these tests, argues that they can be interpreted with accuracy only if an infinite number of tests are made, and, further, that there is no logical basis for choosing a level of significance. Being arbitrary, there is no standard way of selecting the appropriate significance level. Despite these reasons, Camilleri maintains that social scientists should still use the tests because they have been useful in other sciences. He does state, however, that they should be used to test theories and not specific hypotheses. Gold, in a recent article (1969), asserts that we can never meet the conditions of random sampling of subjects or random sampling of questions. Both are necessary for the representativeness of the sample. Response rates to questionnaires and interviews are almost always substantially less than 100 per cent, and even those responding fail to answer, or give vague uncodable answers, to specific questions. Despite the universal violation of the condition of random sampling, Gold still advocates the use of tests against a "random process model" to help determine the substantive significance of a relation. Finally, Winch and Campbell (1969) maintain that, out of 15 factors or "threats" that may undermine a design's validity, significance tests are helpful only in the internal validity problem of "instability" (that is, sampling variation, measurement unreliability, and "autonomous instability of repeated or 'equivalent' measures") (p. 141). Tests provide a formal decisionmaking technique for the problem of analyzing the haphazard or systematic covariation in the data. If the conclusion is reached that the covariation is systematic, then this finding becomes "worthy of interpretative attion (p. 140) ." The authors mentioned above, under one condition or another, advocate the use of significance tests because they provide a rigorous statistical technique to handle error rates and probability statements about populations, given knowledge of samples. In other words, these tests provide logically deduced probability statements specifying the degree of error in moving from knowledge of a part to knowledge of the whole. They allow researchers to handle error in a rigorous and interpretable way. The nature of the con-