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A common measure of association between two variables x and y is the bivariate Pearson correlation coefficient rho(x,y) that characterizes the strength and direction of any linear relationship between x and y. This article describes how to determine the optimal sample size for bivariate correlations,reviews available methods, and discusses their different ranges of applicability. A convenient equation is derived to help plan sample size for correlations by confidence interval analysis. In addition, a useful table for planning correlation studies is provided that gives sample sizes needed to achieve 95% confidence intervals (CI) for correlation values ranging from 0.05 to 0.95 and for CI widths ranging from 0.1 to 0.9. Sample size requirements are considered for planning correlation studies.
Moinester et al. (Mon,) studied this question.