Which test compares means from the same group measured twice?

Which test compares means from the same group measured twice?

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

Answer: (A)

When measurements come from the same group taken twice, the two data sets are paired because each second measurement relates to the same individual. The appropriate test is the paired (related samples) t-test. It focuses on the differences within each pair rather than comparing two independent groups. By looking at the average difference between the two measurements and how variable those differences are, this test asks whether the mean change is different from zero. The test computes the difference for each pair, then treats those differences as a single sample. The statistic is the mean of the differences divided by the standard error of the differences, with degrees of freedom equal to the number of pairs minus one. A key assumption is that these differences are approximately normally distributed. This approach is more powerful than treating the two measurements as independent because it removes a lot of between-subject variability; each person acts as their own control. In contrast, an independent samples t-test would compare two separate groups and ignore the pairing. A one-sample t-test would compare a single sample to a known value, not two related measurements. ANOVA is used for comparing more than two groups or conditions, whereas the paired t-test handles exactly two related measurements.

When measurements come from the same group taken twice, the two data sets are paired because each second measurement relates to the same individual. The appropriate test is the paired (related samples) t-test. It focuses on the differences within each pair rather than comparing two independent groups. By looking at the average difference between the two measurements and how variable those differences are, this test asks whether the mean change is different from zero.

The test computes the difference for each pair, then treats those differences as a single sample. The statistic is the mean of the differences divided by the standard error of the differences, with degrees of freedom equal to the number of pairs minus one. A key assumption is that these differences are approximately normally distributed.

This approach is more powerful than treating the two measurements as independent because it removes a lot of between-subject variability; each person acts as their own control. In contrast, an independent samples t-test would compare two separate groups and ignore the pairing. A one-sample t-test would compare a single sample to a known value, not two related measurements. ANOVA is used for comparing more than two groups or conditions, whereas the paired t-test handles exactly two related measurements.