Kurtosis indicates what about the tails of a distribution?

Kurtosis is a statistical measure that focuses specifically on the shape of a distribution, particularly in terms of its tails and the peak of the distribution. It quantifies the "tailedness" of the distribution, which refers to how much of the data is found in the tails relative to a normal distribution.

When discussing tails, kurtosis indicates their weight, meaning it highlights how heavy or light the tails are compared to a normal distribution. A distribution with high kurtosis has heavier tails and a sharper peak, indicating that extreme values (outliers) are more likely to occur. In contrast, a distribution with low kurtosis has lighter tails and a flatter peak, suggesting that extreme values are less likely.

The other options focus on aspects that kurtosis does not measure directly. Length refers to the physical distance which is not captured by kurtosis. Symmetry relates to how balanced the distribution is around its mean, which is more accurately assessed through skewness. Central tendency pertains to the location of the data points within the distribution (like mean, median, or mode), which does not relate to how the tails behave. Therefore, the correct interpretation of kurtosis is that it specifically indicates the weight of the tails of a distribution.