Which concept describes the direction and degree of asymmetry in a distribution?

Which concept describes the direction and degree of asymmetry in a distribution?

• A. Skewness
• B. Central Tendency
• C. Mean
• D. Range

Answer: (A)

Skewness describes the direction and degree of asymmetry in a distribution. It tells us whether the data cluster more on one side of the center and how long the tails are on each side. A longer tail to the right means positive skewness; a longer tail to the left means negative skewness. When skewness is near zero, the distribution is roughly symmetric. Central tendency describes the central value (like the mean), but it doesn’t tell us about the shape or asymmetry. The mean is a measure of that center, and the range measures how spread out the data are from minimum to maximum; neither capture asymmetry. So skewness uniquely describes both direction and degree of asymmetry.

Skewness describes the direction and degree of asymmetry in a distribution. It tells us whether the data cluster more on one side of the center and how long the tails are on each side. A longer tail to the right means positive skewness; a longer tail to the left means negative skewness. When skewness is near zero, the distribution is roughly symmetric. Central tendency describes the central value (like the mean), but it doesn’t tell us about the shape or asymmetry. The mean is a measure of that center, and the range measures how spread out the data are from minimum to maximum; neither capture asymmetry. So skewness uniquely describes both direction and degree of asymmetry.