How to calculate variance in SQL

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

In SQL, variance over the sample table is calculated with the query below. On the sample data it returns 49.03.

Sample variance = 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.

Table

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

The query

Query

SELECT ROUND(VAR_SAMP(website_orders), 2) AS var_orders
FROM daily;

Expected result on the sample data: 49.03.

What the result means

Variance is in squared units (orders²), which is hard to picture. Report the standard deviation to people and use variance in calculations. It is also the quantity behind analysis of variance (ANOVA) and behind portfolio-style questions such as how much total daily orders vary when website and app orders are added together.

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 variance misleads

VAR.S (sample, n − 1) and VAR.P (population, n) differ; choose .S for business data.
Squaring makes it very sensitive to outliers.
Do not compare variances of series measured in different units; use the coefficient of variation.

The plain-language explanation is on the Variance 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

Same method, other tools

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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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