Skewness measures how lopsided a distribution is. Positive skew means a long tail of high values; negative skew a long tail of low values; zero means roughly symmetric.
Skewness = n ÷ ((n − 1)(n − 2)) × Σ ((x − mean) ÷ sd)³
On the 30-day sample below: 2.19.
When to use it
Most business numbers (order values, customer spend, delivery times) are positively skewed. Knowing that tells you to prefer the median and to be careful with averages and z-scores.
Worked example
The single sale day gives this series a clear positive skew. Without it the daily orders are close to symmetric. That is typical: in business data a strong skew is often a handful of special days or customers, so find them before deciding the whole series is lopsided.
=SKEW(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
- Skewness is very sensitive to a few values; one outlier can create it.
- Excel SKEW, pandas .skew() and DuckDB skewness() use the same adjusted formula; SKEW.P and SciPy's default do not.
- With fewer than about 20 rows it is too noisy to read much into.
Calculate it yourself
Related methods
See all statistics methods or the business metrics they are used on.
Further reading
Questions people ask
- What is skewness?
- Skewness measures how lopsided a distribution is. Positive skew means a long tail of high values; negative skew a long tail of low values; zero means roughly symmetric.
- What is the formula for skewness?
- Skewness = n ÷ ((n − 1)(n − 2)) × Σ ((x − mean) ÷ sd)³. On the 30-day sample data the result is 2.19.
- How do you calculate skewness in Excel?
- Use =SKEW(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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