What does a t-test primarily test for?

A t-test is specifically designed to evaluate whether there is a statistically significant difference between the means of two groups. This statistical test is frequently used in hypothesis testing, where researchers aim to determine if the observed differences between two sample means can be attributed to random chance or if they reflect a true difference in the population means.

The t-test accomplishes this by calculating the t-statistic, which takes into account the means of the two groups, the sample sizes, and the standard deviation. This enables the researcher to compare the means directly and assess whether they are significantly different based on the data collected.

In contrast, the other options address different concepts in statistical analysis. Significant relationships between multiple variables pertain more to correlation or regression analysis. Evaluating variance among three or more groups typically involves techniques such as ANOVA. Examining the frequency distribution of a dataset relates to descriptive statistics rather than the comparison of means. Hence, the t-test's primary focus on assessing differences in means distinctly confirms its relevance in answering the question.