# Analysis When the Unit of Assignment and the Unit of Analysis Are Different

There are two common scenarios where the experiment assignment unit differs from the analysis unit:

1. Measuring session-level metrics for a user-level experiment. Ratio metrics are commonly used to solve this (this doc).
2. Measuring logged-in metrics (eg. revenue) on a logged-out experiment. There are two solutions: a. Running the experiment at the device-level, with device-level metrics collected even after the user is logged-in. b. Using ID resolution.

We will explain how to set up the first scenario with Warehouse Native in this doc.

## Example: Organizations and Users​

Scenario:

• Your metrics source has both org_id and user_id.
• The relationship between org_id and user_id is 1-to-many. A single org_id can be associated with multiple users (user_id), but a user_id is only associated with a single org_id.
• Your experiment is assigned at the org_id level.
• You are interested in understanding the treatment effect at the user_id level, such as revenue per user.

### 1. Setup the metric source with org_id as an ID type.​

• In this table, each row of data should have both org_id and user_id.

### 3. Define your metric of revenue per user_id.​

• Your denominator should be count distinct user_id instead of unit count, because the latter is equivalent to count distinct org_id in an org_id level experiment.

## Statistics in the backend​

In the Stats Engine, we utilize the delta method to calculate variance and confidence intervals.

• For mean metrics, we record a value indicating the number of observations per exposed unit in the records column of the staging data. This acts as the denominator or cluster-size value for delta calculations.
• For general ratio metrics, we monitor the two-component metrics (the ratio and the denominator) as independent metrics and combine them during the pulse analysis to derive a single metric from them.

For more information on how we apply the delta method, visit: Statsig - Delta Method Methodology.. The reason we choose to use the delta method is to account for the covariance between the numerator and the denominator (i.e. more users per org is correlated with more revenue). See section 3 of this paper for details.

This approach is also relevant for analyzing event-level outcomes, such as average purchase value, where randomization occurs at the user level, and each user may experience multiple session events.