Kurtosis: Skewness and kurtosis are

Skewness and kurtosis are statistical measures that describe the shape of a data distribution. While skewness indicates the asymmetry of the distribution, kurtosis measures the heaviness of its tails compared to a normal distribution. Kurtosis measures how scores are distributed in a frequency curve. Learn about the three types of kurtosis : mesokurtic, leptokurtic, and platykurtic, and see how they differ from the normal distribution. Kurtosis refers to the “ tailedness ” or the degree of extremity in the tails of a distribution. It provides a measure of how outliers or extreme values appear in your dataset. While variance and standard deviation tell you how spread out the data is, kurtosis tells you how concentrated or dispersed data is in the tails (extreme ends). 1. The underlying logic is straightforward: kurtosis represents the average (or expected value) of standardized data raised to the fourth power. Standardized values less than 1—corresponding to data within one standard deviation of the mean (where the peak occurs)—contribute minimally to kurtosis .