What characteristic best describes the T-test for independent means?

The T-test for independent means is best described as a parametric test that assumes the data being analyzed is normally distributed. This characteristic is crucial because parametric tests are based on population parameters and make specific assumptions about the underlying data, namely that it follows a normal distribution.

In practice, while the T-test can be used for smaller sample sizes, it is particularly robust and widely accepted for moderate to large sample sizes provided the normality assumption is met. The T-test measures the difference between the means of two independent groups, making it an essential tool when the assumption of normality holds, which is often validated through statistical tests or visual inspection of histograms or Q-Q plots.

The other options highlight concepts that don't apply to the independent T-test. Nonparametric tests, for example, are used when the assumptions of normality are not met or when dealing with ordinal data, while paired samples refer to scenarios where the same subjects are measured twice, which is not applicable in this context. Additionally, the notion of it being limited to small sample sizes does not accurately represent the test's capabilities, as it accurately estimates the means comparison across both small and larger samples when assumptions are satisfied.