Kurtosis measures how heavy the tails of a distribution are compared with a normal distribution. Excess kurtosis above zero means more extreme values than a bell curve would produce.
Excess kurtosis (sample-adjusted), as computed by Excel KURT and pandas .kurt()
On the 30-day sample below: 8.52.
When to use it
Heavy tails mean surprises are more common than averages and standard deviations suggest, which matters for stock levels, risk limits and capacity.
Worked example
One extreme day in thirty gives this series a high kurtosis. It is a flag to look at the tails directly, not a number to manage. In practice, plot a histogram or list the most extreme values: that shows what is in the tail, which a single kurtosis figure cannot.
=KURT(C2:C31)| Day | Website orders |
|---|---|
| 1 | 24 |
| 2 | 27 |
| 3 | 22 |
| 4 | 30 |
| 5 | 28 |
| 6 | 25 |
| 7 | 31 |
| 8 | 29 |
| 9 | 26 |
| 10 | 33 |
| 11 | 30 |
| 12 | 28 |
| 13 | 35 |
| 14 | 32 |
| 15 | 29 |
| 16 | 34 |
| 17 | 61 |
| 18 | 31 |
| 19 | 36 |
| 20 | 33 |
| 21 | 30 |
| 22 | 37 |
| 23 | 35 |
| 24 | 32 |
| 25 | 38 |
| 26 | 36 |
| 27 | 34 |
| 28 | 40 |
| 29 | 37 |
| 30 | 39 |
Common mistakes
- Excel and pandas report excess kurtosis (normal = 0); some tools report plain kurtosis (normal = 3).
- It is dominated by the most extreme values and is unstable in small samples.
- High kurtosis does not mean a "peaked" distribution; it is about the tails.
Calculate it yourself
Related methods
See all statistics methods or the business metrics they are used on.
Further reading
Questions people ask
- What is kurtosis?
- Kurtosis measures how heavy the tails of a distribution are compared with a normal distribution. Excess kurtosis above zero means more extreme values than a bell curve would produce.
- What is the formula for kurtosis?
- Excess kurtosis (sample-adjusted), as computed by Excel KURT and pandas .kurt(). On the 30-day sample data the result is 8.52.
- How do you calculate kurtosis in Excel?
- Use =KURT(C2:C31) with the data in rows 2 to 31. The same formula works in Google Sheets.
About the data on this page
The figures come from a small made-up dataset, printed above in full so you can check every number by hand. Download it as daily.csv. The results are computed from those rows when the page is built, and the Excel formulas, SQL and Python on these pages are run against the same rows before publishing, so the code and the answers agree.
To get these numbers from your own data without writing formulas, try AnalyzeIt: ask in plain words and it shows the working behind every figure.
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