Beyond the mean and spread: SKEW measures asymmetry (which tail is longer) and KURT measures tailedness (how heavy the tails are). Together they describe a distribution’s shape.
The example
Distribution shape in two numbers.
| A | B | |
|---|---|---|
| 1 | Measure | Value |
| 2 | Skewness | +0.8 (right tail) |
| 3 | Kurtosis | +1.2 (heavy) |
The formula
The formula:
How it works
How it works:
SKEWis positive when the right tail is longer (income data), negative when the left tail is, and near 0 for symmetric data.KURTis excess kurtosis: 0 matches a normal distribution, positive means heavier tails (more outliers), negative means lighter.- Use them to judge whether mean-based methods fit, or whether the data is lopsided or outlier-prone.
- Both need a reasonable sample size to be meaningful.
When skew is high, the mean gets pulled toward the long tail — the median often describes the “typical” value better. High kurtosis warns that extreme values are more likely than a normal model assumes.
Try it: interactive demo
Values.
Variations
Kurtosis
Tailedness:
Mean vs median
Skew indicator:
SKEW.P (365)
Population skew:
Pitfalls & errors
Need enough data. Skew and kurtosis are noisy for small samples.
KURT is excess. Excel returns kurtosis minus 3, so 0 = normal, not 3.
Outliers dominate. A single extreme value strongly affects both measures.
Practice workbook
Frequently asked questions
How do I measure skewness in Excel?
What does KURT return?
How do I tell if data is skewed without SKEW?
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