How does a one-way ANOVA differ from a t-test?

A one-way ANOVA is designed to compare the means of three or more groups, which allows researchers to determine if there are statistically significant differences among those group means. In contrast, a t-test is typically used when comparing the means of only two groups. This fundamental difference is crucial in determining the appropriate statistical test based on the number of groups being analyzed.

While both tests assess differences in means, the t-test is limited to two groups, making it unsuitable for situations where there are three or more categories under consideration. The use of ANOVA in these cases allows for a single test to evaluate multiple groups simultaneously, thereby controlling for Type I error that could occur if multiple t-tests were conducted instead.

Understanding this distinction is essential for accurately analyzing data in experimental designs, especially when determining the appropriate test for your hypothesis regarding group means.