A/B Test Significance Calculator
Compare a control and variation to estimate conversion lift and whether the result is statistically significant enough to trust.
Version A conversion rate
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Version B conversion rate
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Lift
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Significance confidence
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Summary
Key insights
How A/B test significance works
A/B significance testing helps founders avoid making decisions based on random variation. This calculator compares two conversion rates and estimates whether the observed difference is likely meaningful.
What this calculator covers
- Control and variation conversion rates
- Lift percentage between the two versions
- Estimated confidence level using a two-proportion z-test
- A plain-English recommendation on whether the result looks reliable
Why founders use this
- To avoid ending tests too early
- To see whether a winning variation is probably real or just noise
- To make better decisions on landing pages, ads, and email funnels
- To connect optimisation work to more reliable commercial outcomes
Common questions
Quick answers to common founder questions related to this tool.
What significance should I aim for in an A/B test?
Many teams look for around 95% confidence before calling a result statistically significant, though context, traffic, and risk tolerance matter.
Can a higher conversion rate still be inconclusive?
Yes. A variation can show a better conversion rate but still lack enough data for the result to be trustworthy.