Which test is used for a parametric difference between two related sets of scores (repeated measures or matched pairs)?

Which test is used for a parametric difference between two related sets of scores (repeated measures or matched pairs)?

• A. Related (paired) t test.
• B. Independent samples t test.
• C. The single-sample t test.
• D. ANOVA for repeated measures.

Answer: (A)

Two related sets of scores are analyzed with the paired t-test. The core idea is that the two measurements come from the same subjects or from matched pairs, so you don’t treat the two samples as independent. Instead, you look at the difference within each pair and ask whether the average difference is zero. This difference score is treated as a single sample, assumed to be approximately normally distributed, and the test computes whether its mean is significantly different from zero. The t statistic is the mean of the differences divided by the standard deviation of those differences, scaled by the square root of the number of pairs. If the resulting p-value is below the chosen alpha, you conclude there is a statistically significant difference between the two related measurements. This approach is appropriate for repeated measures or matched-pairs designs. If you had two independent groups, you’d use an independent-samples t-test. If you’re comparing your sample mean against a known population value rather than a paired difference, you’d use a single-sample t-test. If there are more than two related measurements (for example, multiple time points or conditions), you’d typically use ANOVA for repeated measures. When there are only two related measurements, the paired t-test is the standard, straightforward method.

Two related sets of scores are analyzed with the paired t-test. The core idea is that the two measurements come from the same subjects or from matched pairs, so you don’t treat the two samples as independent. Instead, you look at the difference within each pair and ask whether the average difference is zero. This difference score is treated as a single sample, assumed to be approximately normally distributed, and the test computes whether its mean is significantly different from zero. The t statistic is the mean of the differences divided by the standard deviation of those differences, scaled by the square root of the number of pairs. If the resulting p-value is below the chosen alpha, you conclude there is a statistically significant difference between the two related measurements.

This approach is appropriate for repeated measures or matched-pairs designs. If you had two independent groups, you’d use an independent-samples t-test. If you’re comparing your sample mean against a known population value rather than a paired difference, you’d use a single-sample t-test. If there are more than two related measurements (for example, multiple time points or conditions), you’d typically use ANOVA for repeated measures. When there are only two related measurements, the paired t-test is the standard, straightforward method.