How to calculate mean in SQL

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

In SQL, mean over the sample table is calculated with the query below. On the sample data it returns 32.73.

Mean = Sum of all values ÷ Number of values

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(AVG(website_orders), 2) AS mean_orders
FROM daily;

Expected result on the sample data: 32.73.

What the result means

Here the mean is pulled up a little by day 17, a one-day sale with 61 orders. When one or two values are far from the rest, compare the mean with the median before quoting it.

Notes for SQL

Tested on SQLite and DuckDB; it uses only standard SQL, so it also runs on PostgreSQL, MySQL and BigQuery.
Statistical aggregates ignore NULL rows. Decide whether a missing day should be NULL (left out) or 0 (counted as a zero day).

Where mean misleads

One extreme value can move the mean a long way. The median is not affected by how extreme the extremes are.
An average of averages is not the overall average unless every group is the same size. Weight by size, or add up first.
Blank cells are ignored by AVERAGE but zeros are not. A day with no orders recorded as blank raises the average.

The plain-language explanation is on the Mean (average) 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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