> ## Documentation Index > Fetch the complete documentation index at: https://statsig-4b2ff144-serverless-cloudflare.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # One-Sample Test ## One-Sample Tests (aka Fixed-Value Test) A one-sample test compares a single sample of data against a known or hypothesized value to determine if 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 (e.g., feature usage, error types), compare against expected baseline * **Algorithm Testing**: Test if an algorithm performs better than random (e.g., testing if success rate differs from 50%) ## Statistical Considerations One-sample tests provide a way to make statistical inferences about whether your observed data differs significantly from a hypothesized value. The test helps determine if any observed difference is due to random variation or represents a true change in the underlying process. ## How to Enable the Feature 1. Go to the setup page of an experiment image 2. Click the metric name image 3. Select Use Fixed Baseline as Control image