Which term is used to describe a distribution feature where the tail extends to the left?

Which term is used to describe a distribution feature where the tail extends to the left?

• A. Negative Skew
• B. Positive Skew
• C. Outlier
• D. Central Tendency

Answer: (A)

Skewness describes the asymmetry of a distribution. When the tail extends to the left, the distribution is negatively skewed. This means most data pile up on the right, while a few smaller values stretch the left side, pulling the tail in that direction. Because of this, the mean tends to be pulled toward those lower values, while the median stays closer to the bulk of the data. The term for this feature is negative skewness. In contrast, a tail to the right signals positive skewness; an outlier is just one extreme value, not a description of the overall tail direction; central tendency refers to a measure of center like the mean or median, not the tail shape.

Skewness describes the asymmetry of a distribution. When the tail extends to the left, the distribution is negatively skewed. This means most data pile up on the right, while a few smaller values stretch the left side, pulling the tail in that direction. Because of this, the mean tends to be pulled toward those lower values, while the median stays closer to the bulk of the data. The term for this feature is negative skewness. In contrast, a tail to the right signals positive skewness; an outlier is just one extreme value, not a description of the overall tail direction; central tendency refers to a measure of center like the mean or median, not the tail shape.