# A/B experiments
Source: https://sovaria.in/docs/ab-experiments

Split users between values of a remote config parameter and measure which one wins on an analytics event or on SQL over your own tables.

An experiment runs on one parameter. Units (signed-in users, otherwise devices) that match its condition and fall in its exposure share are assigned a variant by weight. Assignments are stored, so changing weights only moves people who weren't assigned yet. The SDK logs one `$exposure` event the first time the app reads the value, and results compare each variant with the control.

CLI:
```bash
sov experiments create checkout_test --param new_checkout \
  --variant control=false:50 --variant on=true:50 --goal purchase
sov experiments start checkout_test
sov experiments results checkout_test
sov experiments rollout checkout_test --yes        # the winner becomes the default
```

Goal in SQL:
```bash
# Mean order value per user, from your own orders table (paid plans)
sov experiments create price_test --param price_banner --variant control=a --variant b=b \
  --metric mean --goal-sql "select user_id, total as value from orders where created_at > now() - interval '30 days'"
```

## How results are computed

- Conversion goals use a two-proportion z-test; mean goals use Welch's t-test. Both show a 95% interval for the change against the control.
- A **sample-ratio check** (chi-square) flags a split that doesn't match the weights; fix logging or assignment before trusting the numbers.
- No winner is named until every variant has the minimum sample (100 by default) and p < 0.05.
- **Roll out winner** makes the winning value the parameter's default for everyone, ends the experiment and publishes.

## Plans and limits

|  | Free | Pro | Ultra | Team | Enterprise |
|---|---|---|---|---|---|
| A/B experiments running at once per project | 1 | 10 | 25 | 100 | 500 |
| Experiment goal metrics in SQL | — | ✓ | ✓ | ✓ | ✓ |