Summary: This paper considers several confidence intervals for estimating the population coefficient of variation based on parametric, nonparametric and modified methods. A simulation study has been conducted to compare the performance of the existing and newly proposed interval estimators. Many intervals were modified in our study by estimating the variance with the median instead of the mean and these modifications were also successful. Data were generated from normal, chi-square, and gamma distributions for \(\mathrm{CV} = 0.1\), \(0.3\), and \(0.5\). We reported coverage probability and interval length for each estimator. The results were applied to two public health data: child birth weight and cigarette smoking prevalence. Overall, good intervals included an interval for chi-square distributions by A. T. McKay [J. R. Stat. Soc., n. Ser. 95, 695–698 (1932; Zbl 0005.30204)], an interval estimator for normal distributions by E. G. Miller [Commun. Stat. Theory Methods 20, 3351–3363 (1991)], and our proposed interval.