Which test is preferable for evaluating the difference in means when small, independent samples are used?

The T-test for independent means is the preferred method for evaluating the difference in means when working with small, independent samples. This statistical test is specifically designed to compare the means of two separate groups to determine if there is a statistically significant difference between them. It is particularly useful when the samples are small and assumed to follow a normal distribution, which is often a condition when assessing the means of small groups.

The T-test calculates the t-statistic based on the difference in sample means, the variability of the samples, and the size of the samples. This allows researchers to make inferences about the population means when the sample sizes are limited.

In contrast, the Mann-Whitney U test is a non-parametric test that compares the ranks of two independent groups rather than their means, making it less suitable for directly testing mean differences. The T-test for dependent means, also known as the paired T-test, is used when the samples are not independent, such as in before-and-after studies. ANOVA (Analysis of Variance) is used when comparing means across three or more groups rather than just two, which is also not applicable in this scenario of comparing two independent samples.

Therefore, the T-test for independent means stands out as the most appropriate