> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cube.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Calculating averages and percentiles

> Learn how to model and query percentile-based metrics alongside averages so skewed numeric distributions are represented accurately in Cube.

## Use case

We want to understand the distribution of values for a certain numeric property
within a dataset. We're used to average values and intuitively understand how to
calculate them. However, we also know that average values can be misleading for
[skewed](https://en.wikipedia.org/wiki/Skewness) distributions which are common
in the real world: for example, 2.5 is the average value for both `(1, 2, 3, 4)`
and `(0, 0, 0, 10)`.

So, it's usually better to use
[percentiles](https://en.wikipedia.org/wiki/Percentile). Parameterized by a
fractional number `n = 0..1`, where the n-th percentile is equal to a value that
exceeds a specified ratio of values in the distribution. The
[median](https://en.wikipedia.org/wiki/Median) is a special case: it's defined
as the 50th percentile (`n = 0.5`), and it can be casually thought of as "the
middle" value. 2.5 and 0 are the medians of `(1, 2, 3, 4)` and `(0, 0, 0, 10)`,
respectively.

## Data modeling

Let's explore the data in the `users` cube that contains various demographic
information about users, including their age:

| name            | age |
| --------------- | --: |
| Abbott, Breanne |  52 |
| Abbott, Dallas  |  43 |
| Abbott, Gia     |  36 |
| Abbott, Tom     |  39 |
| Abbott, Ward    |  67 |

Calculating the average age is as simple as defining a measure with the built-in
[`avg` type](/reference/data-modeling/measures#type).

Calculating the percentiles would require using database-specific functions.
However, almost every database has them under names of `PERCENTILE_CONT` and
`PERCENTILE_DISC`,
[Postgres](https://www.postgresql.org/docs/current/functions-aggregate.html) and
[Snowflake](https://docs.snowflake.com/en/sql-reference/functions-aggregation)
included. For [BigQuery](https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#approx_quantiles),
you'd need to use the `APPROX_QUANTILES` function.

<CodeGroup>
  ```yaml title="YAML" theme={"dark"}
  cubes:
    - name: users
      # ...

      measures:
        - name: avg_age
          type: avg
          sql: age

        - name: median_age
          type: number
          sql: PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY age)

        - name: p95_age
          type: number
          sql: PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY age)
  ```

  ```javascript title="JavaScript" theme={"dark"}
  cube("users", {
    measures: {
      avg_age: {
        sql: `age`,
        type: `avg`
      },

      median_age: {
        sql: `PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY age)`,
        type: `number`
      },

      p95_age: {
        sql: `PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY age)`,
        type: `number`
      }
    }
  })
  ```
</CodeGroup>

## Result

Using the measures defined above, you can explore statistics about the age of
your users. For a typical dataset, the average age closely matches the median
age, and the 95th percentile reveals the upper bound for the vast majority of
users — for example, if `p95_age` returns 82, then 95% of all users are
younger than 82 years.