How to calculate coefficient of variation in SQL

SQL query for coefficient of variation, run on a downloadable 30-day sample table, with the expected result and which databases support it.

In SQL, coefficient of variation over the sample table is calculated with the query below. On the sample data it returns 21.39%.

Coefficient of variation = Standard deviation ÷ Mean × 100

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.

Table

CREATE TABLE daily (
  day INTEGER,
  ad_spend INTEGER,
  website_orders INTEGER,
  app_orders INTEGER,
  avg_basket INTEGER
);

The query

Query

SELECT ROUND(100.0 * STDDEV_SAMP(website_orders) / AVG(website_orders), 2) AS cv_pct
FROM daily;

Expected result on the sample data: 21.39%.

What the result means

Format the Excel result as a percentage. A CV of around 20% means a typical day is about a fifth away from the average day.

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 coefficient of variation misleads

Only meaningful for values on a ratio scale with a true zero (counts, money). Not for temperatures or scores that can be negative.
When the mean is close to zero the CV explodes and means nothing.
It inherits the standard deviation's sensitivity to outliers.

The plain-language explanation is on the Coefficient of variation page.

The data

daily.csv (made up): 30 days from one store
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

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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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