Which test is associated with the paired-samples t-test?

Which test is associated with the paired-samples t-test?

• A. Independent t-test
• B. Dependent t-test
• C. One-sample t-test
• D. ANOVA

Answer: (B)

When you have two related measurements for the same subjects (for example, before and after a treatment on the same people), you analyze the difference within each subject. The test that matches this situation is the paired-samples (dependent) t-test because it focuses on those paired differences to see if, on average, there is a nonzero change. It computes the mean of the difference scores, the variability of those differences, and uses that to determine if the observed average difference is unlikely to occur by chance. In other words, it tests H0: the mean difference is zero. This is the best choice here because it specifically handles the dependency between the two measurements by analyzing differences within pairs, which increases statistical power compared to treating the two measurements as independent. The other tests don’t fit the scenario: the independent t-test compares two separate groups and ignores pairing; the one-sample t-test compares a single sample mean to a fixed value rather than the mean difference between two related conditions; and ANOVA is for comparing means across three or more groups or conditions (and for two related measurements you’d use the paired t-test for the simplest case).

When you have two related measurements for the same subjects (for example, before and after a treatment on the same people), you analyze the difference within each subject. The test that matches this situation is the paired-samples (dependent) t-test because it focuses on those paired differences to see if, on average, there is a nonzero change. It computes the mean of the difference scores, the variability of those differences, and uses that to determine if the observed average difference is unlikely to occur by chance. In other words, it tests H0: the mean difference is zero.

This is the best choice here because it specifically handles the dependency between the two measurements by analyzing differences within pairs, which increases statistical power compared to treating the two measurements as independent. The other tests don’t fit the scenario: the independent t-test compares two separate groups and ignores pairing; the one-sample t-test compares a single sample mean to a fixed value rather than the mean difference between two related conditions; and ANOVA is for comparing means across three or more groups or conditions (and for two related measurements you’d use the paired t-test for the simplest case).