eng TQMP The Quantitative Methods for Psychology 1913-4126 2014-09-01 10 2 124 130 10.20982/tqmp.10.2.p124 article Moinester, Murray murraym@tauphy.tau.ac.il 1 Gottfried, Ruth ruth@gottfrieds.com 2 Tel Aviv University University of Haifa 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. https://www.tqmp.org/RegularArticles/vol10-2/p124/p124.pdf sample size estimation correlation confidence interval R statistical environment