A/B Test Calculator
Enter your test data and get statistical significance, charts, and plain-English explanations of every metric.
Control (A)
Rate: 5.00%
Variant (B)
Rate: 5.50%
Not Yet Significant
Not enough evidence at 95% confidence. p-value is 11.3%, above your 5% threshold. Run the test longer.
Conversion Rates
Distribution Curves
Blue = Control, Green = Variant. Less overlap = more significant.
Statistical Power
Shows how power increases with sample size. Dashed line = 80% power target.
Sample Size vs Effect Size
Smaller effects need exponentially more visitors to detect.
+10.00%
0.1129
1.5852
35.4%
4.57% - 5.43%
5.05% - 5.95%
How to use
- Enter the number of visitors and conversions for your Control (A) group
- Enter the same for your Variant (B) group
- Choose your confidence level (95% is standard)
- Read the verdict, explore the chart, and click the info icons on each metric for detailed explanations
Understanding the Results
What is statistical significance?
It tells you whether the difference in conversion rates between your two variants is likely real or just due to random chance. At 95% confidence, it means there's only a 5% probability the result is noise.
What is a p-value?
The p-value is the probability of seeing the observed difference (or larger) if there were actually no difference between A and B. Lower p-value = stronger evidence that B is different from A. Typically, p < 0.05 is considered significant.
How many visitors do I need?
It depends on your baseline conversion rate and the minimum effect you want to detect. As a rule of thumb: smaller effects require more visitors. The calculator shows a recommended sample size based on your current data.