Which statement best describes the sign test?

Which statement best describes the sign test?

• A. It uses the magnitude of the differences to determine significance.
• B. It requires the data to be normally distributed.
• C. It tests whether two unrelated samples are different.
• D. It analyses only the sign of the differences, ignoring magnitude.

Answer: (D)

The sign test focuses on the direction of change between paired observations, not how big the changes are. It counts how many differences are positive and how many are negative and uses those signs to assess whether there is a real tendency away from zero. Because it only uses the sign, the magnitude of the differences is ignored, which is why it’s nonparametric and does not assume normality. It’s designed for paired data (or a single sample compared to a median), not for comparing two unrelated groups. So the statement that best describes the sign test is that it analyzes only the sign of the differences, ignoring magnitude. The other ideas—relying on magnitude, requiring normal distribution, or comparing two independent samples—do not capture the essence of this test.

The sign test focuses on the direction of change between paired observations, not how big the changes are. It counts how many differences are positive and how many are negative and uses those signs to assess whether there is a real tendency away from zero. Because it only uses the sign, the magnitude of the differences is ignored, which is why it’s nonparametric and does not assume normality. It’s designed for paired data (or a single sample compared to a median), not for comparing two unrelated groups. So the statement that best describes the sign test is that it analyzes only the sign of the differences, ignoring magnitude. The other ideas—relying on magnitude, requiring normal distribution, or comparing two independent samples—do not capture the essence of this test.