How to calculate kurtosis in SQL

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

In SQL, kurtosis over the sample table is calculated with the query below. On the sample data it returns 8.52.

Excess kurtosis (sample-adjusted), as computed by Excel KURT and pandas .kurt()

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(KURTOSIS(website_orders), 3) AS kurt_orders
FROM daily;

Expected result on the sample data: 8.52.

What the result means

One extreme day in thirty gives this series a high kurtosis. It is a flag to look at the tails directly, not a number to manage. In practice, plot a histogram or list the most extreme values: that shows what is in the tail, which a single kurtosis figure cannot.

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 kurtosis misleads

Excel and pandas report excess kurtosis (normal = 0); some tools report plain kurtosis (normal = 3).
It is dominated by the most extreme values and is unstable in small samples.
High kurtosis does not mean a "peaked" distribution; it is about the tails.

The plain-language explanation is on the Kurtosis 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

Related methods

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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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