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

# Power Calculator

> Calculate required sample sizes and estimated durations for A/B experiments based on statistical power analysis.

## POST /api/v1/experiments/power-calculator

Calculates the required sample size and estimated experiment duration for an A/B test. Uses a two-proportion z-test formula with traffic estimates derived from the tenant's recent interaction history.

### Request Body

| Field                     | Type   | Required | Description                                                          |
| ------------------------- | ------ | -------- | -------------------------------------------------------------------- |
| `baselineConversionRate`  | number | Yes      | Current conversion rate (0.001-1.0)                                  |
| `minimumDetectableEffect` | number | Yes      | Smallest effect size to detect (0.001-1.0)                           |
| `significanceLevel`       | number | No       | Alpha level for the test (0.01-0.20, default: 0.05)                  |
| `power`                   | number | No       | Statistical power (0.5-0.99, default: 0.8)                           |
| `trafficSplit`            | number | No       | Proportion of traffic in the treatment group (0.1-0.9, default: 0.5) |

### Example

```bash theme={null}
curl -X POST https://playground.kaireonai.com/api/v1/experiments/power-calculator \
  -H "Content-Type: application/json" \
  -H "X-Tenant-Id: my-tenant" \
  -d '{
    "baselineConversionRate": 0.05,
    "minimumDetectableEffect": 0.02,
    "significanceLevel": 0.05,
    "power": 0.8,
    "trafficSplit": 0.5
  }'
```

### Response

```json theme={null}
{
  "requiredSampleSize": 7850,
  "perVariant": 3925,
  "estimatedDuration": {
    "days": 16,
    "basedOnDailyTraffic": 500
  },
  "recommendation": "With 500 daily decisions, you need ~16 days to reach statistical significance.",
  "sensitivityTable": [
    { "effect": 0.01, "sampleSize": 31400, "days": 63 },
    { "effect": 0.02, "sampleSize": 7850, "days": 16 },
    { "effect": 0.05, "sampleSize": 1260, "days": 3 },
    { "effect": 0.10, "sampleSize": 320, "days": 1 }
  ]
}
```

### Response Fields

| Field                                   | Type            | Description                                      |
| --------------------------------------- | --------------- | ------------------------------------------------ |
| `requiredSampleSize`                    | integer         | Total sample size needed across both variants    |
| `perVariant`                            | integer         | Sample size per variant                          |
| `estimatedDuration.days`                | integer or null | Estimated days to reach the required sample size |
| `estimatedDuration.basedOnDailyTraffic` | integer         | Average daily decisions from the last 14 days    |
| `recommendation`                        | string          | Human-readable guidance                          |
| `sensitivityTable`                      | array           | Sample sizes for different effect sizes          |

### Sensitivity Table

The sensitivity table shows how the required sample size changes with different minimum detectable effects. This helps teams understand the trade-off between experiment duration and the ability to detect small effects.

<Tip>
  If `estimatedDuration.basedOnDailyTraffic` is 0, the tenant has no recent decision traffic. Start sending decisions through the Recommend API to get accurate duration estimates.
</Tip>

### Roles

admin, editor, viewer

See also: [Experiments](/api-reference/experiments) | [Algorithm Models](/api-reference/algorithm-models)