A/B Test Calculator
Calculate statistical significance for A/B tests. Input visitors and conversions to see if your results are reliable.
Control (A)
Rate: 3.00%
Variation (B)
Rate: 3.80%
Frequently Asked Questions
What is statistical significance in A/B testing?▼
Statistical significance means the difference between your control and variation is unlikely to be due to random chance. At 95% confidence, there's only a 5% probability the observed difference happened by chance. Most teams use 95% as the minimum threshold for making decisions.
How many visitors do I need for an A/B test?▼
It depends on your baseline conversion rate and the minimum effect you want to detect. As a rough guide: to detect a 10% relative improvement on a 5% baseline conversion rate with 95% confidence, you need about 30,000 visitors per variation. Smaller effects need larger samples.
Should I use one-tailed or two-tailed tests?▼
This calculator uses a two-tailed test, which is the recommended approach. A two-tailed test checks if the variation is significantly different (better or worse) than the control. One-tailed tests are less conservative and can lead to false conclusions.
When should I stop my A/B test?▼
Stop when you reach statistical significance AND your predetermined sample size. Don't peek and stop early when results look good — this inflates false positive rates. Set your sample size before starting and commit to running the full test.
Test which variation wins — then make sure the content is actually good
A/B testing finds the winning variant. TeamBench makes sure the content behind it meets your standards. Score landing pages, emails, and ad copy before they go live.
- Score landing page and email content for quality
- Custom criteria: brand voice, clarity, CTA strength
- Consistent quality across every variation you test
- Free to start — no credit card required