Indexes and boundaries for "quantitative significance" in statistical decisions.
Boundaries for delta, representing a "quantitatively significant" or "substantively impressive" distinction, have not been established, analogous to the boundary of alpha, usually set at 0.05, for the stochastic or probabilistic component of "statistical significance". To determine what boundaries are being used for the "quantitative" decisions, we reviewed pertinent articles in three general medical journals. For each contrast of two means, contrast of two rates, or correlation coefficient, we noted the investigators' decisions about stochastic significance, stated in P values or confidence intervals, and about quantitative significance, indicated by interpretive comments. The boundaries between impressive and unimpressive distinctions were best formed by a ratio of greater than or equal to 1.2 for the smaller to the larger mean in 546 comparisons, by a standardized increment of greater than or equal to 0.28 and odds ratio of greater than or equal to 2.2 in 392 comparisons of two rates; and by an r value of greater than or equal to 0.32 in 154 correlation coefficients. Additional boundaries were also identified for "substantially" and "highly" significant quantitative distinctions. Although the proposed boundaries should be kept flexible, indexes and boundaries for decisions about "quantitative significance" are particularly useful when a value of delta must be chosen for calculating sample size before the research is done, and when the "statistical significance" of completed research is appraised for its quantitative as well as stochastic components.
Burnand
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B
Department of Medicine, Robert Wood Johnson Clinical Scholars Program, Yale University School of Medicine, New Haven, CT 06510-8025.
Kernan
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WN
Feinstein
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AR
D016001:Confidence Intervals; D003657:Decision Making; D006801:Humans; D016017:Odds Ratio; D010506:Periodicals as Topic / Q000592:standards*; D012015:Reference Standards; D012680:Sensitivity and Specificity; D013269:Stochastic Processes