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A simulation study was conducted to examine the performance of several confidence intervals (CIs) for Kendall's tau ( t xy ) under a variety of population conditions. Two normal population variables ( N = 10,000) were transformed to have tau correlations, τ = 0, .19, .41, or.71. Samples ( n = 10, 50, 200) were drawn from the transformed populations 2000 times under each level of correlation, and accompanying CIs were computed on each sample. The results show that the CI for τ based on a consistent estimate of the variance of t xy has the best coverage and power among a number of alternatives. Kendall's t xy is unaffected by non‐normality induced by monotonic transformations and, with its consistent variance estimated from the sample, performs well under a wide range of conditions.
Long et al. (Thu,) studied this question.