ab-test-analysis

To calculate statistical significance for A/B test results, the tool computes p-values, confidence intervals, and power checks for control vs. variant groups. It analyzes conversion, engagement, and guardrail metrics to determine if a variant meaningfully improves outcomes.

Yes, you can analyze A/B test data from a CSV or Excel export. The tool supports flexible data intake from these formats and analytics exports, generating Python scripts for calculations when needed to produce a significance report.

The best way to translate A/B test statistics into product decisions is by assessing guardrail considerations alongside significance. The tool delivers clear ship, extend, or stop recommendations based on p-values and confidence intervals.

To assess guardrail metrics during A/B testing, the tool evaluates them alongside conversion and engagement metrics across defined durations and traffic splits. It includes a guardrail assessment in the final interpretation to prevent negative impacts.

Yes, A/B test analysis supports experiments with custom traffic splits. The tool applies statistical rigor to quantify significance across defined durations and traffic splits, ensuring accurate product decisions for your specific experimental setup.