Experimentation tool
A/B Test Calculator
Compare two versions side by side and find out which one wins, and whether the result is real or just luck.
A/B Test Calculator
Formula
Conversions ÷ Visitors, compared across both versions
Divide each version’s conversions by its visitors to get its conversion rate, then see which rate is higher. The calculator goes a step further and tells you whether that gap is a real difference or just random chance.
What is an A/B Test Calculator?
An A/B test calculator compares two versions of something, like a page, email, or button, and shows which one gets better results. More importantly, it tells you whether that difference is real or just random. This lets you make decisions based on evidence instead of a hunch.
Conversions
How many took action?
Conversions are the people who did what you wanted, like signing up, buying, or clicking. This is the win you’re measuring for each version. Counting only real, completed actions keeps your test accurate.
Conversion Rate
Which version performs better?
The conversion rate is the share of visitors who converted, found by dividing conversions by visitors. It’s the number you actually compare between two versions. A higher rate looks like the winner, but you still need to check if the gap is real.
Statistical Significance
Can you trust the result?
Statistical significance tells you whether the difference between the two versions is real or could have happened by chance. A common target is 95% confidence, which means you can be fairly sure the result will hold.
From guesswork to tested decisions
Know what’s worth testing before you build it.
ProductBridge helps you collect feedback from across your channels, shape it into a clear roadmap, and share every shipped change through a built-in changelog. So instead of guessing what to test next, you start with ideas your customers have already pointed you toward.
How to Use an AB Test to Make Better Decisions?
Split your audience so each group sees one version, then track how many visitors and conversions each version gets. Enter those numbers into the calculator to get each conversion rate and see whether the difference between them is real.
The result is only useful if the test is set up well. Run it long enough to collect data, don’t stop the moment one version looks ahead, and change only one thing at a time. A clear, significant result gives you the confidence to roll out the winning version, while an unclear one usually means you need more data.
RELATED TOOLS
More Free SaaS Calculators & Tools
More free calculators and ProductBridge tools to help you grow your SaaS.
AB Test calculator FAQ
Answers to common questions about running A/B tests, measuring statistical significance, and making data-driven product decisions.
How do you calculate statistical significance in an AB test?
Statistical significance compares the conversion rates of your two versions and checks how likely the difference is to be real rather than down to chance. It depends on both the size of the gap between them and how many visitors each version had. The bigger your sample and the cleaner the difference, the more you can trust it.
What sample size do you need for an AB test?
There’s no single number, because it depends on your current conversion rate and how big a change you’re trying to detect. Small improvements need much larger samples to prove, while big differences show up with fewer visitors. Working out your sample size before you start tells you how much traffic you’ll need, so you don’t stop the test too early.
How long should you run an AB test?
Run your test long enough to collect enough visitors and to cover full weeks, usually at least one to two weeks. Full weeks matter because people act differently on weekdays than on weekends. Wait until the test has run its full course before picking a winner.
Why do my AB test results keep changing?
At the start, the numbers jump around a lot because there isn’t much data yet, so the version that’s ahead today can fall behind tomorrow. As more visitors come in, the results settle. Changing results simply means the test needs more time before the real winner shows.
What are the limitations of AB testing?
AB testing tells you which version wins, but not why people preferred it. It also needs a lot of traffic to give trustworthy results, which makes it hard for small audiences. And since you test one change at a time, finding real improvements can be slow. AB testing shows you what performs better, but it works best alongside customer feedback, which explains the reasons.