Landing Page A/B Test Significance Calculator

Determine if your A/B test results are statistically significant. Enter your visitor and conversion data to see which version performs better.

Total visitors to original version

Conversions from original version

Total visitors to new version

Conversions from new version

Z-Score = |p1 - p2| / sqrt(p(1-p)(1/n1 + 1/n2)); Significance at 95%: Z > 1.96
Control: 5000 visitors, 150 conv (3%). Variant: 5000 visitors, 180 conv (3.6%). Absolute diff: 0.6%, Z-score: 2.15, Significant at 95%! 20% relative lift with confidence.

What does statistical significance mean in A/B testing?

Statistical significance means the difference in conversion rates is likely real and not due to random chance. At 95% confidence, there's only a 5% chance the result occurred randomly. Higher confidence = more certainty but requires more traffic.

How many visitors do I need for an A/B test?

Depends on baseline conversion rate and desired lift. For 20% lift detection at 95% confidence: 2% baseline needs ~15K per variation, 5% baseline needs ~4K, 10% baseline needs ~2K. Use sample size calculators before starting to ensure valid results.

What is a good conversion rate improvement?

A positive lift of 5%+ is worth implementing in most cases. Even 2-5% can be significant at scale. However, consider the practical impact: a 2% lift on 100K monthly visitors = 2,000 extra conversions. If each conversion = $50, that's $100K/year.

When should I stop an A/B test?

Stop when: you reach statistical significance, you hit minimum sample size, or results are clearly negative. Avoid stopping early just because one variant is winning - that increases false positive rates. Run tests for full business cycles (at least 1-2 weeks).