Which statement best describes how skewness affects the mean and median?

Which statement best describes how skewness affects the mean and median?

• A. Positive skew implies mean < median
• B. Positive skew implies mean > median
• C. Skewness has no effect on mean or median
• D. Negative skew implies mean > median

Answer: (B)

Skewness shifts how the mean compares to the median because the mean is pulled by extreme values, while the median stays near the center of the data. In a positively skewed distribution, a few very large values stretch the tail to the right and pull the mean upward, making it larger than the median. The median isn’t affected as much by those outliers, so it sits lower. That’s why the statement that the mean exceeds the median in positive skew is the best description. Conversely, in negative skew the tail is on the left and the mean is pulled downward, so it would be less than the median. Skewness does impact the mean relative to the median rather than having no effect.

Skewness shifts how the mean compares to the median because the mean is pulled by extreme values, while the median stays near the center of the data. In a positively skewed distribution, a few very large values stretch the tail to the right and pull the mean upward, making it larger than the median. The median isn’t affected as much by those outliers, so it sits lower. That’s why the statement that the mean exceeds the median in positive skew is the best description. Conversely, in negative skew the tail is on the left and the mean is pulled downward, so it would be less than the median. Skewness does impact the mean relative to the median rather than having no effect.