## One-sample tests (fixed-value test)
A one-sample test compares a single sample of data against a known or hypothesized value to determine whether there is a statistically significant difference. Unlike A/B tests that compare two groups, one-sample tests evaluate whether a single group differs from a specific benchmark, target, or historical baseline.
## When to use one-sample tests
One-sample tests are useful for comparing a single group against a known value:
- **Single Group Events**: When only one group can trigger certain events (for example, feature usage or error types), compare against an expected baseline.
- **Algorithm Testing**: Test whether an algorithm performs better than random (for example, whether a success rate differs from 50%).
## Statistical considerations
One-sample tests provide a way to make statistical inferences about whether observed data differs significantly from a hypothesized value. The test helps determine whether any observed difference is due to random variation or represents a true change in the underlying process.
## Enable fixed-value baseline comparison
1. Go to the setup page of an experiment
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2. Click the metric name
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3. Select **Use Fixed Baseline as Control**
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