## Metric Insights and Aggregated Impact

Statsig's Insights page provides a clear view of how experiments and feature gates impact a specific metric. It helps answer key questions such as "How much impact have I driven?" and helps diagnose unexpected changes in metrics.

Insights presents a reverse perspective of the [Pulse](/experiments/interpreting-results/read-results) view. While Pulse measures the impact of a new feature on all your metrics, Insights lets you focus on a single metric and identify which tests are impacting it the most. This is useful for assessing your team's impact and setting realistic goals.

## How to read Insights

1. Navigate to the Insights section on the Statsig console: https://console.statsig.com/ . Insights is also available in the insight tab for each metric.
2. Select a metric from the selector drop-down at the top of the page.
3. Select the ID type, time window, and other filters.
4. The page shows the relative impact, topline impact, and projected launch impact for any experiment or gate that includes this metric.
5. Statsig sums the projected launch impacts, adjusts based on false positive risk ('winner's curse'), and displays the result as the Aggregated Impact Estimate.

{% figure %}
![Insights dashboard showing aggregated impact estimates for a metric](/images/snippets/pulse/aggregated-impact/430563dc-4794-4d69-a314-36c76a6fcf74.png)
{% /figure %}

## How the math works

Go to the [topline and projected launch impact calculations](/experiments/statistical-methods/topline-impact#computing-projected-launch-impact) for details.

To estimate false positive risk and calculate Aggregated Impact, Statsig uses the methodology in this [paper](https://dl.acm.org/doi/10.1145/3534678.3539160), which is widely adopted across the industry. Specifically:

$$
Aggregated Impact=\sum\_\{i}\{(1 - FPR\_i) \times Projected Launch Impact\_i}
$$

The [projected launch impact](/experiments/statistical-methods/topline-impact) is an estimate of the topline impact assuming a decision is made and the test group is launched to all users. The false positive risk is calculated by the following formula:

$$
FPR\_i = \frac\{\alpha\_i \times \pi}\{\alpha\_i \times \pi + (1 - \beta\_i) \times (1 - \pi)}
$$

In this formula, $\alpha\_i$ is the significance level for experiment i, $\beta\_i$ is the type II error, and 1 - $\pi$ is the prior success rate based on historical experiment results.