The measure of variability for positively skewed data?

The measure of variability for positively skewed data?

• A. Standard deviation
• B. Range
• C. Variance
• D. Interquartile range

Answer: (D)

When a distribution is positively skewed, the long right tail pulls extreme values upward, which inflates measures that rely on all data points, like the mean and standard deviation. The interquartile range looks at the spread of the middle 50% of the data (from the 25th to the 75th percentile) and ignores extreme values in the tail. Because it is not affected by outliers, the IQR provides a more stable and representative sense of variability for skewed data. Meanwhile, standard deviation, variance, and range can be distorted by the high end of the distribution.

When a distribution is positively skewed, the long right tail pulls extreme values upward, which inflates measures that rely on all data points, like the mean and standard deviation. The interquartile range looks at the spread of the middle 50% of the data (from the 25th to the 75th percentile) and ignores extreme values in the tail. Because it is not affected by outliers, the IQR provides a more stable and representative sense of variability for skewed data. Meanwhile, standard deviation, variance, and range can be distorted by the high end of the distribution.