When conducting a T-test, how many groups are being analyzed?

In a T-test, two groups are being analyzed to determine if there is a statistically significant difference between their means. This statistical method is commonly used in scenarios where researchers want to compare the means of two distinct groups—for example, comparing the test scores of students from two different teaching methods or the effectiveness of a medication between two patient groups.

The T-test operates under the assumption that the data from both groups are normally distributed and that the variances of the two groups are equal (in the case of the independent t-test). By focusing on two groups, the T-test provides insights into how the independent variable impacts the dependent variable across these two populations, making it a fundamental tool in hypothesis testing within research.

In contrast, options suggesting one, three, or multiple groups would correspond to different types of analyses, such as a one-sample T-test for one group, a one-way ANOVA for three or more groups, or more complex multivariate approaches for multiple groups. None of these alternatives apply to the standard T-test, which distinctly involves the comparison of two groups only.