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Stating a confidence interval is a traditional method of indicating the sampling error of a point estimator of a model's performance measure. We propose a single dimensionless criterion, inspired by Schruben's coverage function, for evaluating and comparing the statistical quality of confidence-interval procedures. Procedure quality is usually thought to be multidimensional, composed of the mean (and maybe the variance) of the interval-width distribution and the probability of covering the performance measure (and maybe other values). Our criterion, which we argue lies at the heart of what makes a confidence-interval procedure good or bad, compares a given procedure's intervals to those of an "ideal" procedure. For a given point estimator (such as the sample mean) and given experimental data process (such as a first-order autoregressive process with specified parameters), our single criterion is a function of only the sample size (or other rule that ends sampling).