Workflow Automation Templates
A library of ready-to-use workflow templates to accelerate your data journey

Skewness
Measure asymmetry in data distribution

Overview
This workflow calculates skewness and kurtosis to analyze the shape and symmetry of data distributions, helping assess data normality and detect outliers.
Details
A left-skewed (negative) distribution has a longer left tail with the mean less than the mode, while a right-skewed (positive) distribution has a longer right tail with the mean greater than the mode. The sign of skewness indicates direction—zero represents no skewness, negative means left skew, and positive means right skew. The larger the value, the more the distribution deviates from normality. Kurtosis measures the “tailedness” or “peakedness” of a distribution. Excess kurtosis equal to zero indicates a normal distribution, positive values (leptokurtic) represent heavy tails with more outliers, and negative values (platykurtic) represent light tails with fewer outliers.