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AI A/B Test Significance Calculator

Intelligently calculate A/B test statistical significance, supporting conversion rate, mean, multivariate test analysis with auto-recommended sample sizes and test duration

Calculator Interface

Interactive calculator will be available soon

Features

  • ✓ Supports conversion rate (A/B test), mean comparison, multivariate test (MVT) and more
  • ✓ AI auto-calculates P-value, confidence interval, statistical power to determine significance
  • ✓ Smart sample size calculator recommends minimum samples based on expected lift
  • ✓ Supports both Bayesian and Frequentist statistical methods for different analysis needs
  • ✓ Visual result display with distribution charts, cumulative confidence curves and more

How to Use

  1. Select test type (conversion rate / mean / multivariate)
  2. Input sample sizes and conversion data for control and experiment groups
  3. Set significance level (default α=0.05) and statistical power (default 80%)
  4. Click calculate to view significance results and AI optimization suggestions

FAQ

What is statistical significance?

Statistical significance indicates that results are unlikely due to random chance. When P-value is below 0.05, we consider results statistically significant — meaning 95% confidence that a real difference exists between groups.

How long should an A/B test run?

Test duration depends on traffic volume and expected lift. Our AI sample size calculator auto-recommends minimum samples and test duration based on your baseline conversion rate and expected improvement, typically 1-4 weeks.

What's the difference between Bayesian and Frequentist methods?

Frequentist gives fixed P-values and confidence intervals, suitable for rigorous hypothesis testing. Bayesian provides 'probability that B beats A', more intuitive for quick decisions. Both converge with large samples.

How to handle multivariate tests?

Multivariate testing (MVT) tests combinations of multiple variables simultaneously. Our tool supports full and fractional factorial designs, auto-calculating main effects and interactions to find the optimal combination.

What if results are not significant?

If results aren't significant, AI analyzes possible causes: insufficient sample size, short test duration, small effect size, etc. It recommends increasing samples or adjusting experiment design with recalculated resource needs.