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

# Exploration

> Drill down into the results of an experiment to learn more about the impact.

Confidence gives you the ability to run analyses on an experiment for
exploratory purposes, also called Explorations. Because this type of analysis significantly increases the
risk of seeing false positives, you should never use it to decide the success or
failure of an experiment. Rather, use the results to inspire hypotheses to test
in new experiments.

You see all existing explorations in the **Exploration** section on the right sidebar. For ended
experiments, you can also create an exploration directly from **Explore** in the
[Spotlight](./analyze-results#spotlight) section.

## Metrics and Dimensions

To produce results, you need to add metrics to your analysis. You can select any metric that uses
the same entity that you configured in the [metric configuration section](/docs/experiments/metrics#assignments) for your experiment.

For each metric, you can add dimensions if they exist. Dimensions come from
[dimension tables](/docs/metrics/dimension-tables) that use the same entity as the metric.
Use dimensions to find out if the experiment had a different impact
on different subgroups.

### Static Dimensions and Dimensions that Change over Time

Confidence supports both static dimensions and dimensions that change over time.

Static dimensions don't change rapidly over time and
aren't affected by the experiment treatment. For example, user age,
registration country, preferred language, etc.

If the experiment treatment can affect a dimension value, it's a time-changing
dimension. For example, user's subscription status, consumption patterns, feature
usages, etc.

To ensure that segmentation is not misleading for time-changing dimensions,
Confidence always uses the dimension value at the time right before the
user got exposed to the treatment. This way, the treatment doesn't
influence the dimension value itself.

<Note>
  If users don't have dimension values in the table, they get NULL as
  dimension values. Make the result interpretations easier by mapping NULL values
  to a meaningful value in the dimension table configuration.
</Note>

### Examples of Dimensions That Change over Time

#### New User vs Existing User

The dimension table has a column `is_existing_user` that has a true value for
all existing users at a given time. Since new users that come in during the
experiment don't have a value on this dimension table before exposure by
construction, they get the value NULL. You can map the NULL value to "New user"
in the dimension-table configuration to make the interpretation of the
exploration easier.

You also need to decide on when to consider a user "New" relative to their first
exposure. For example, you can use a 1-day window if a user needs to have signed
up within 24 hours before exposure to be in the "New" category.

#### User that Took a Certain Action the Week Before the Experiment

Examples of this action could be users that watched a
video, added something to checkout, used a certain feature in your app, etc.

The dimension table has a column `took_action_x_last_7_days` that captures a
rolling seven-day window and only uses the last value. If you produce this data
hourly, you can use a one-hour window in the window setting for the dimension in
the dimension-table configuration.

<Note>
  If your dimension value requires aggregations, perform those directly in the dimension table query or before. Confidence only uses the latest non-NULL value in the time window.
</Note>

If instead the table has a column `took_action_x` with hourly values in the
dimension table and you configure Confidence to use a 7-day window to
fetch the dimension value, **Confidence uses the last non-NULL value
in that 7-day window, which doesn't correspond to the goal of this example.**

## Stale Analyses

Sometimes your analysis might end up in a `Stale` state. If you hover over the
status, you see a message describing why the analysis is stale. Two reasons are:

1. New data exists, and results might be different if you run
   the calculations again. To calculate new results based on all available data, click **Retrigger**
   above the results.
2. The conclusion is older than the results. Retriggering result calculations because
   of the first reason is a common explanation for this. It means that the results might have changed and the
   conclusion is possibly invalid. Update the conclusion to get rid of this warning.

## Related Resources

<CardGroup cols={2}>
  <Card title="Explore Results" href="/docs/how-to-guides/explore-results">
    Step-by-step exploration guide
  </Card>

  <Card title="Dimension Tables" href="/docs/metrics/dimension-tables">
    Configure segmentation dimensions
  </Card>

  <Card title="Analyze Results" href="/docs/experiments/analyze-results">
    Results analysis overview
  </Card>
</CardGroup>