A/B Test Calculator
Running an A/B test without checking statistical significance is like flipping a coin and calling it a trend. Our A/B Test Calculator tells you whether the difference between your control and variant is real - or just the result of chance. Enter your visitor and conversion numbers, choose your confidence level, and get an instant, clear result.
Why Use Our Ab Test Calculator?
- Calculates statistical significance instantly from your A/B test data.
- Supports confidence levels of 90%, 95%, and 99%.
- Works for any split test - landing pages, CTAs, email subject lines, ad copy, and more.
- Clear pass/fail verdict so you know whether to implement your variant or keep testing.
- Completely free with no account needed.
How to Use the Tool:
- Enter the number of visitors and conversions for your control (version A).
- Enter the number of visitors and conversions for your variant (version B).
- Select your desired confidence level.
- Click “Calculate” to see whether your result is statistically significant.
A/B Test Calculator
Statistical significance tells you how confident you can be that the difference in performance between your two versions is real, not random. A result that isn’t statistically significant means you need more data before making a decision - acting too early leads to bad conclusions and wasted optimisation effort.
Frequently Asked Questions
What is statistical significance in A/B testing?
Statistical significance is a measure of how confident you can be that the difference in results between your control and variant is real and not due to random variation. A 95% confidence level means there is only a 5% chance the result occurred by chance. Most marketing and CRO professionals use 95% as their minimum threshold.
How many conversions do I need before testing?
There’s no fixed number, but as a general rule you need at least 100 conversions per variant before your results become meaningful. The lower the conversion rate, the more traffic you need. Running a test too early - before you have enough data - is one of the most common A/B testing mistakes.
What should I do if my result is not significant?
Keep running the test. A non-significant result means the data collected so far isn’t conclusive - it doesn’t mean your variant isn’t working, just that you don’t have enough evidence yet. Resist the temptation to end the test early based on early trends.
Can I test more than two versions at once?
This calculator is designed for standard A/B tests comparing two versions. For multivariate tests or tests with three or more variants, the statistical calculations become more complex and you may need a specialist tool.