Kurtosis is best described as a measure of

• A. The variance of the distribution.
• B. The value of the distribution's kurtosis.
• C. A measure of peakedness or tail heaviness relative to the normal distribution.
• D. The sample size.

Answer: (C)

Kurtosis measures how the shape of a distribution compares to a normal curve in terms of how peaked it is and how heavy the tails are. A distribution with high kurtosis has a sharper peak and fatter tails (more extreme values) than the normal, while a distribution with low kurtosis has a flatter peak and thinner tails. This makes kurtosis about the overall form of the tails and the peak, not about how spread out the data are (that’s variance), not about the numeric value of the statistic itself, and not related to sample size. So the best description is that kurtosis measures peakedness or tail heaviness relative to the normal distribution. The normal distribution is often used as a reference point, with its own kurtosis value, and distributions can be classified as leptokurtic, platykurtic, or mesokurtic based on how their tails compare.