> For the complete documentation index, see [llms.txt](https://developer.harness.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developer.harness.io/feature-management-experimentation/use-fme/experimentation/setup/metric-selection/index.md).

# Metric selection

Choosing the right metrics is a critical step in setting up any experiment. Harness FME supports three categories of metrics that help you evaluate impact, monitor for regressions, and uncover secondary insights. Each metric type plays a distinct role in how you interpret and act on your experiment results.

You can assign metrics to an experiment on the **Experiments** page or in a feature flag's **Metrics impact** tab.

| Metric type                                                                                                                    | Definition                                                                         | Notes                                                                      |
| ------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------- | -------------------------------------------------------------------------- |
| [Key metrics](/feature-management-experimentation/use-fme/experimentation/setup/metric-selection/key-metrics.md)               | Primary indicators of success. Evaluate whether the experiment met its goal.       | Can trigger alerting.                                                      |
| [Guardrail metrics](/feature-management-experimentation/use-fme/experimentation/setup/metric-selection/guardrail-metrics.md)   | Protect critical business, performance, or UX metrics from unintended regressions. | Subject to account-wide alerting. Automatically applied to matching flags. |
| [Supporting metrics](/feature-management-experimentation/use-fme/experimentation/setup/metric-selection/supporting-metrics.md) | Provide additional context and help you understand secondary trends.               | Useful for exploring unexpected results or validating assumptions.         |