Eight common but false objections to the discontinuation of significance testing in the analysis of research data.

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Abstract

Logically and conceptually, the use of statistical significance testing in the analysis of research data has been thoroughly discredited. However, reliance on significance testing is strongly embedded in the minds and habits of researchers, and therefore proposals to replace significance testing with point estimate estimates and confidence intervals often encounter strong resistance. This chapter examines eight of the most commonly voiced objects to reform of data analysis practices and shows each of them to be erroneous. The objections are: (a) Without significance tests we would not know whether a finding is real or just due to chance; (b) hypothesis testing would not be possible without significance tests; (c) the problem is not significance tests but failure to develop a tradition of replicationg studies; (d) when studies have a large number of relationships, we need significance tests to identify those that are real and not just due to chance; (e) confidence intervals are themselves significance tests; (f) significance testing ensures objectivity in the interpretation of research data; (g) it is the misuse, not the use, of significance testing that is the problem; and (h) it is futile to try to reform data analysis methods, so why try? Each of these objections is intuitively appealing and plausible but is easily shown to be logically and intellectually bankrupt. The same is true of the almost 80 other objects we have collected. Statistical significance testing retards the growth of scientific knowledge; it never makes a positive contribution. After decades of unsuccessful efforts, it now appears possible that reform of data analysis procedures will finally succeed. If so, a major impediment to the advance of scientific knowledge will have been removed.