In SQL, standard deviation over the sample table is calculated with the query below. On the sample data it returns 7.
Sample standard deviation = √( Σ (x − mean)² ÷ (n − 1) )
Step by step
- Load daily.csv into a table called daily (day, ad_spend, website_orders, app_orders, avg_basket).
- Run the query and compare with the expected result.
CREATE TABLE daily (
day INTEGER,
ad_spend INTEGER,
website_orders INTEGER,
app_orders INTEGER,
avg_basket INTEGER
);The query
SELECT ROUND(STDDEV_SAMP(website_orders), 2) AS sd_orders
FROM daily;Expected result on the sample data: 7.
What the result means
Use the sample standard deviation (dividing by n − 1) when your rows are a sample of a longer process, which business data almost always is.
Notes for SQL
- Tested on DuckDB. The same functions exist in PostgreSQL. SQLite and MySQL lack most statistical aggregates; compute in Python or load the data into DuckDB, which can query a CSV file directly.
- Statistical aggregates ignore NULL rows. Decide whether a missing day should be NULL (left out) or 0 (counted as a zero day).
Where standard deviation misleads
- STDEV.S and STDEV.P differ: .S divides by n − 1 (a sample), .P by n (a whole population). pandas .std() is the sample version; NumPy np.std() is the population version by default.
- One extreme value inflates it. Here day 17 adds a lot; without it the standard deviation is much smaller.
- It assumes the mean is a sensible centre. For skewed data, the interquartile range describes spread more honestly.
The plain-language explanation is on the Standard deviation page.
The data
| 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 |
Same method, other tools
Related methods
Learn more about SQL
- Python and SQL for data analysis, side by side — 3 min read
- Data analysis methods and techniques, and when to use each — 3 min read
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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