A/B Test Design Agent
Designs statistically sound A/B and multivariate test plans across marketing channels, calculating sample sizes and flagging invalid test setups.
Marketing teams frequently run A/B tests that are underpowered, stopped too early, or testing multiple variables at once without realizing it, leading to false-positive 'wins' that don't hold up when rolled out broadly
Designing a statistically valid test requires calculating sample size, expected runtime, and minimum detectable effect, math that most marketers skip in favor of gut-feel decisions
This agent takes a test hypothesis and current baseline metrics, then calculates the required sample size and runtime for statistical validity, flags confounding variables in the proposed setup, and generates a structured test brief ready for implementation
It also monitors live tests and warns when a team is about to call a result before reaching significance
The agent takes a test hypothesis, target metric, baseline conversion rate, and available traffic volume, then applies standard statistical power calculations to determine minimum sample size, expected test duration, and minimum detectable effect at a chosen confidence level. It reviews the proposed test setup for common validity issues (multiple simultaneous changes, insufficient randomization, seasonal confounds) and flags them before launch. Once a test is live, it ingests real-time results and continuously recalculates statistical confidence, alerting the team if someone attempts to call a winner before the pre-registered stopping criteria are met.
Capture Test Hypothesis
- Document hypothesis, target metric, and expected direction of change
- Pull current baseline conversion rate and traffic volume
- Identify the channel and audience segment for the test
- Confirm success metric and secondary metrics
Calculate Statistical Requirements
- Calculate minimum sample size for target confidence level
- Estimate test runtime based on available traffic
- Determine minimum detectable effect size
- Flag if available traffic makes the test impractical
Validate Test Design
- Check for confounding variables or overlapping tests
- Verify single-variable isolation or note multivariate design
- Check for seasonal or campaign-timing conflicts
- Flag any randomization or tracking setup issues
Monitor Test Integrity
- Track real-time results against pre-registered stopping criteria
- Recalculate statistical confidence continuously
- Alert if a team attempts to call results early
- Log final outcome and confidence level to test archive