What property of a dataset is displayed when there is a high density of values clustered at one end of the distribution?

The correct choice reflects the concept of skewness, which measures the asymmetry of the probability distribution of a real-valued random variable. When a dataset exhibits a high density of values clustered at one end, it indicates that the distribution is not symmetrical; instead, one tail is longer or fatter than the other.

In a positively skewed distribution, values cluster towards the lower end, with a long tail extending towards the higher values. Conversely, in a negatively skewed distribution, values cluster towards the higher end, with a tail stretching towards the lower values. Therefore, when observing how data points are distributed, skewness effectively captures this asymmetry and the extent to which one side of the distribution is stretched out compared to the other.

Other properties like kurtosis, which describes the sharpness of the distribution peak, and dispersion, which refers to the spread of values within a dataset, do not specifically address the clustering of values towards one end of the distribution. Variance quantifies the degree to which data points differ from the mean, again not focusing on skewness or the asymmetry of the distribution. Thus, skewness is the most relevant measure to describe high density clustering at one end of the distribution.