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

# Configure Variance Reduction

> Use pre-exposure data in the Stats API to reduce variance and increase experiment precision.

**Configure Variance Reduction**

Use pre-exposure data in the Stats API to reduce variance and increase experiment precision.

Variance reduction uses pre-exposure data to reduce noise in your experiment metrics. This allows you to detect effects with fewer samples or detect smaller effects with the same sample size.

## How It Works

The Stats API applies regression adjustment to reduce variance. You provide pre-exposure measurements alongside your post-exposure data, and the API uses the correlation between them to produce more precise estimates.

## Configure Variance Reduction for Analysis

Include pre-exposure data in the `adjustment` field within your summary data:

```json theme={null}
{
  "id": "my-metric",
  "segments": [
    {
      "dimensions": {},
      "groups": [
        {
          "group": "control",
          "data": {
            "zTest": {
              "summary": {
                "mean": 10.5,
                "variance": 25.0,
                "count": 1000,
                "adjustment": {
                  "mean": 9.8,
                  "variance": 22.0,
                  "covariance": 15.2
                }
              }
            }
          }
        },
        {
          "group": "treatment",
          "data": {
            "zTest": {
              "summary": {
                "mean": 11.2,
                "variance": 26.0,
                "count": 1000,
                "adjustment": {
                  "mean": 9.9,
                  "variance": 21.5,
                  "covariance": 14.8
                }
              }
            }
          }
        }
      ]
    }
  ]
}
```

### Adjustment Fields

The `adjustment` object contains:

| Field | Description |
| - | - |
| `mean` | Mean of the pre-exposure measurements |
| `variance` | Variance of the pre-exposure measurements |
| `covariance` | Covariance between pre-exposure and post-exposure values |

## Configure Variance Reduction for Power Analysis

For power analysis, include the expected covariance adjustment in your power data:

```json theme={null}
{
  "hypotheses": [
    {
      "id": "my-metric",
      "segments": [
        {
          "dimensions": {},
          "powerData": {
            "zTest": {
              "baselineMean": 10.0,
              "baselineVariance": 25.0,
              "adjustment": {
                "baselineVariance": 22.0,
                "baselineCovariance": 15.0
              }
            }
          }
        }
      ]
    }
  ]
}
```

### Power Analysis Adjustment Fields

| Field | Description |
| - | - |
| `baselineVariance` | Variance of the pre-exposure covariate |
| `baselineCovariance` | Covariance between the pre-exposure covariate and the outcome |

## Interpret Results

The API returns both adjusted and unadjusted estimates. The treatment effect estimate benefits from the reduced variance, providing tighter confidence intervals. The response includes a `varianceReductionRate` field showing the achieved reduction.

## Related Resources

> **[Variance Reduction](/docs/capabilities/experiments/statistics/variance-reduction.md)**
>
> Learn about variance reduction concepts

> **[Run an Analysis](/docs/reference/api/how-to-guides/stats/run-analysis.md)**
>
> Complete analysis tutorial

> **[Run Power Analysis](/docs/reference/api/how-to-guides/stats/run-power-analysis.md)**
>
> Calculate required sample sizes