Which statement describes a negatively skewed distribution?

Which statement describes a negatively skewed distribution?

• A. Right tail longer.
• B. Left tail longer.
• C. Symmetric distribution with no skew.
• D. Kurtosis is high.

Answer: (B)

Negativity in skewness means the tail extends more to the left. So a distribution that is negatively skewed has a longer left tail, with most data clustered toward higher values and a tail reaching down to the lower values. This leftward tail pulls the mean toward the lower end, while the median sits closer to the bulk of the data. In contrast, a longer right tail would indicate positive skew, and a symmetric shape has no skew. Kurtosis describes how peaked or heavy-tailed the distribution is, not the direction of skew. So describing a negatively skewed distribution by a longer left tail captures the essential idea.

Negativity in skewness means the tail extends more to the left. So a distribution that is negatively skewed has a longer left tail, with most data clustered toward higher values and a tail reaching down to the lower values. This leftward tail pulls the mean toward the lower end, while the median sits closer to the bulk of the data. In contrast, a longer right tail would indicate positive skew, and a symmetric shape has no skew. Kurtosis describes how peaked or heavy-tailed the distribution is, not the direction of skew. So describing a negatively skewed distribution by a longer left tail captures the essential idea.