![]() We claim that the use of such tests do not provide any advantages in relation to citation indicators, interpretations of them, or the decision making processes based upon them. We argue that applying statistical significance tests and mechanically adhering to their results is highly problematic and detrimental to critical thinking. ![]() The issues specifically discussed are the ritual practice of such tests, their dichotomous application in decision making, the difference between statistical and substantive significance, the implausibility of most null hypotheses, the crucial assumption of randomness, as well as the utility of standard errors and confidence intervals for inferential purposes. Based on examples from articles by proponents of the use statistical significance tests in research assessments, we address some of the numerous problems with such tests. Statistical significance tests are highly controversial and numerous criticisms have been leveled against their use. This paper raises concerns about the advantages of using statistical significance tests in research assessments as has recently been suggested in the debate about proper normalization procedures for citation indicators. These functions provide an easy to use solution to the difficult problem of calculating and displaying within-subject confidence intervals. Free open-source, cross-platform software for these interval estimates and plots (and for some common alternatives) is provided in the form of R functions for one-way within-subject and two-way mixed ANOVA designs. In situations where both types of inference are of interest, the use of a two-tiered CI is recommended. The latter can be accomplished by fitting a multilevel model. ![]() It is argued that the former are best accomplished by adapting intervals proposed by Cousineau (2005) and Morey (2008) so that non-overlapping confidence intervals for individual means correspond to a confidence for their difference that does not include zero. A key distinction is between intervals supporting inference about patterns of means (and differences between pairs of means in particular) and those supporting individual means. The psychological and statistical literature contains several proposals for calculating and plotting confidence intervals for within-subject (repeated measures) ANOVA designs. ![]()
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