A/B TEST
SIGNIFICANCE
Check whether the observed lift between two conversion variants is likely to be real or just noise.
Experiment Inputs
When to Trust It
Make sure both variants ran under the same conditions and for the same audience.
Tiny samples often produce dramatic uplifts that disappear as more data comes in.
Decide in advance which conversion matters most so you do not cherry-pick the winner after the fact.
Conversion Experiment
A/B Significance
1000 visitors total
1000 visitors total
Statistically significant
Variant B is ahead at 95% confidence with a relative uplift of +60.00%.
The calculator uses a two-proportion z-test with a pooled standard error, which is the common quick check for A/B conversion tests.
Export / Save
Export a clean experiment summary for growth reviews, product decisions, or stakeholder recaps.
WHY USE THIS A/B SIGNIFICANCE CALCULATOR?
Two-Variant Comparison
Compare conversion rates for control and test variants side by side
Confidence Controls
Switch between 90%, 95%, and 99% confidence assumptions quickly
Significance Check
Use a two-proportion z-test to judge whether the observed lift is meaningful
Lift Summary
See relative uplift, z-score, and the current winner in one output card
Export Snapshot
Share the test readout with product, growth, or leadership teams
Input Validation
Catch impossible states such as conversions exceeding visitors before reading the result