Skewness measures the asymmetry of a distribution. Positive skew means the tail is on the right; negative skew means the tail is on the left.
⚠Perfectly symmetric data has skewness = 0. Skewness > 1 or < -1 indicates substantial asymmetry.
What is Skewness?
Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. It tells us the direction and relative magnitude of the deviation from symmetry.
Positive Skew
Right tail longer, mean > median > mode
Negative Skew
Left tail longer, mean < median < mode
Symmetric
Zero skew, mean = median = mode
Distribution Shape
Complements mean and standard deviation
💡 Example: Data [2,4,6,8,10,12,14,16,18,20] is symmetric, skewness ≈ 0. If data is [1,2,3,4,5,6,7,8,9,100], it's right-skewed.
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