Simple distribution

Skewness and kurtosis are important statistical measures that help us understand the shape of a data distribution beyond simple averages. Skewness shows whether the data is symmetrical or tilted to one side, indicating if most values are concentrated on the left or right with a long tail on the opposite side. For example, income data is usually positively skewed, where many people earn around the average but a few earn extremely high amounts. Kurtosis, on the other hand, tells us about the “peakedness” and tail thickness of the distribution. A high kurtosis means more extreme values or outliers, while a low kurtosis indicates a flatter, more uniform spread of data. Together, skewness and kurtosis help analysts and researchers check if data follows a normal distribution, detect outliers, and choose the right statistical tools for accurate decision-making.