Comparison of Test Statistics for Mean Difference Testing Between Two Independent Populations

Abstract

The purpose of the article is to evaluate the efficiency of seven test statistics for mean difference testing between two independent populations. The evaluation was based on the probability of type I error and power of the test at 0.05 significance level under population distributions assumed to be normal, exponential, log-normal, gamma, and Laplace with equal sample sizes, and both equal and unequal variances. The results showed that for equal variance, the test statistics with the highest testing power controlled the probability of type I error were Z-test for normal and exponential distributions, Welch based on rank test (WBR) for log-normal and gamma distributions, and Mann-Whitney U test (MWU) for Laplace distribution. For unequal variance, Z-test was more efficient under normal, exponential, log-normal, and gamma distributions, while WBR was appropriate for Laplace distribution.