ExperimentGuard

Validate A/B test methodology and flag statistical pitfalls before decisions are made

Analytics AIFreemium

ExperimentGuard reviews A/B test configurations and results for statistical validity issues including insufficient sample size, peeking-induced false positives, sample ratio mismatch, winner's curse bias, and multiple hypothesis testing without correction. The tool provides actionable remediation guidance for each detected issue and a trust score indicating overall result reliability. Data teams use it as a quality gate before shipping experiment winners while analytics leaders use it to establish experimentation standards across the organization.

Key Features

• ✓Validity checking
• ✓Peeking detection
• ✓SRM analysis
• ✓Trust scoring
• ✓Remediation guidance