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

# Sampled Data in Honeycomb

> Find out how Honeycomb adjusts query results to account for sample rate and what to keep in mind when working with sampled data in your datasets.

Learn how Honeycomb adjusts for sample rate when querying sampled data and considerations for working with sampled data.

## How Honeycomb Adjusts for Sample Rate

When you sample your data with our sampling techniques, each span in a trace is given a `SampleRate` attribute that represents `N` when you only sample `1/N` traces.
This allows Honeycomb to weight counts to compensate for the fact that you are sampling your data.

For example, you are doing head sampling at a 10% sampling rate, which means only 10% of traces are exported to Honeycomb:

| Trace ID | Sample Rate (on each span) | duration\_ms |
| -------- | -------------------------- | ------------ |
| abcd1234 | 10                         | 200          |
| 4321dcba | 10                         | 1100         |

In this example, the `SampleRate` attribute is set to `10` because you are sampling 10% of traces, or 1 in 10 traces.
With this information, Honeycomb can correct for sample rate and calculate accurate values for various aggregations:

* `COUNT` of traces: `(2 * 10) = 20`
* `AVG(duration_ms)`: `((200 * 10) + (1100 * 10)) / (10 + 10) = 650`

This means you can send less data and yet still see usefully accurate data in Honeycomb.
Sample rate correction applies to `SUM` and percentile aggregations as well.

By setting the `SampleRate` attribute, your sampling techniques can be as simple or sophisticated as you need, and Honeycomb will do the rest.
If you're using [Refinery](/manage-data-volume/sample/honeycomb-refinery/), this is done automatically for its dynamic samplers.

## `COUNT_DISTINCT` and Sampled Data

[Query Builder's](/investigate/query/build/) `COUNT_DISTINCT` operator does not compensate for sampling rate, so use it with care when working with sampled data.

`COUNT_DISTINCT` estimates the count of distinct values in a field using the [HyperLogLog algorithm](https://en.wikipedia.org/wiki/HyperLogLog) and can only count values that are actually present in the data.

When using `COUNT_DISTINCT` in a query, you can view the `average sample rate` for the query.
Locate it in the metadata below the [result summary table](/investigate/query/build/#select-summary-table) with `elapsed query time` and `rows examined` fields.
`average sample rate` displays the average sample rate across all underlying events included in the query result.

## Query Sampled Data Without Correcting for Sample Rate

Sometimes you may want to query sampled data without taking sample rate into account.
For example, you want to see how many actual events your dynamic sampler sends so you can adjust your sampling strategy.
Or you may need to debug issues with the sampled data.

For these cases, you can use [Usage Mode](/get-started/manage-costs/how-honeycomb-calculates-usage/#usage-mode).
Usage Mode provides a query builder that evaluates queries in an unweighted mode that does not correct for sample rate.
To access Usage Mode:

1. Select **Usage** in the left navigation of the Honeycomb UI
2. Under **Per-environment Breakdown**, select **Usage Mode** for the environment you want to query Calculations in this mode don't correct for sample rates

In Usage Mode, you have access to the `Sample Rate` field, and `COUNT` (and all other calculation operations) are unweighted.