How does a t-test differ from an ANOVA?

How does a t-test differ from an ANOVA?

• A. A t-test compares means between two groups; ANOVA compares means across three or more groups and reduces Type I error inflation.
• B. A t-test compares variances between groups; ANOVA compares means.
• C. A t-test is used for more than two groups; ANOVA is used for two groups.
• D. A t-test uses multiple comparisons; ANOVA uses a single comparison.

Answer: (C)

The main idea is that these tests differ in how many groups you’re comparing. A t-test is used when you want to know if the average outcome differs between two groups. An ANOVA is used when you have three or more groups and you want to know if at least one group’s mean is different from the others. Importantly, ANOVA provides a single overall test of whether any differences exist among the group means, which helps control the chance of finding a difference just by luck when you’d otherwise be doing multiple comparisons. If there are exactly two groups, a t-test and a one-way ANOVA give equivalent conclusions under the same assumptions.

The main idea is that these tests differ in how many groups you’re comparing. A t-test is used when you want to know if the average outcome differs between two groups. An ANOVA is used when you have three or more groups and you want to know if at least one group’s mean is different from the others. Importantly, ANOVA provides a single overall test of whether any differences exist among the group means, which helps control the chance of finding a difference just by luck when you’d otherwise be doing multiple comparisons. If there are exactly two groups, a t-test and a one-way ANOVA give equivalent conclusions under the same assumptions.