Variation A: % conversion rate
Variation B: % conversion rate
Improvement: %
Z-Score:
P-Value:
Confidence:
A/B testing, also known as split testing, is a simple yet powerful method used to compare two versions of a webpage, app interface, advertisement, or any digital element to determine which version performs better. The goal is to make decisions based on actual user behavior rather than assumptions.
In a typical A/B test, your audience is randomly split into two groups. One group sees the original version, called Variation A (or the "control"), and the other sees a modified version, called Variation B. The change in Variation B could be anything — a different button color, new headline, layout adjustment, or even a pricing structure.
By tracking how users interact with each version — such as clicking a button, signing up for a service, or completing a purchase — you can determine which version is more effective in achieving your desired outcome.
A/B testing is widely used in marketing, product design, and user experience (UX) optimization to reduce guesswork and improve digital performance through real-world experiments.
A/B testing is valuable because it removes uncertainty from decision-making. Instead of relying on opinions or gut feelings, you can use real data from actual users to guide your choices. This leads to smarter improvements and better outcomes.
Here are some key benefits of A/B testing:
Whether you're a marketer, business owner, or product manager, A/B testing helps you continuously refine and improve your digital experience by learning what truly works for your audience. Over time, these small, evidence-based changes can lead to significant gains in performance and satisfaction.
This A/B Test Calculator helps you determine whether the difference between two variations (A and B) is statistically significant. It’s easy to use — just follow the steps below to input your data and view the results.
Visitors are the total number of people who were exposed to each variation. For example, if 500 users saw Variation A, that’s your visitor count for A. Similarly, if 600 users saw Variation B, that’s your visitor count for B.
Conversions represent the number of users who completed the action you’re tracking — such as making a purchase, filling out a form, clicking a link, or subscribing to a newsletter. The conversion rate is calculated by dividing conversions by visitors and multiplying by 100 to get a percentage.
Example: If 100 people visited version A and 10 of them converted, the conversion rate is (10 / 100) × 100 = 10%.
Make sure that the number of conversions does not exceed the number of visitors. If it does, the calculator will show an error message and ask you to correct the input.
Once you input your data and click the “Calculate Results” button, the calculator will display key metrics that help you understand how each variation performed. Here’s what each result means:
Conversion Rate is the percentage of visitors who completed your desired action — such as signing up, purchasing, or clicking a button. It’s calculated by dividing the number of conversions by the number of visitors and then multiplying by 100.
For example:
The calculator will show both rates side by side so you can see which version performed better.
Improvement Percentage shows how much better (or worse) Variation B performed compared to Variation A. This metric helps you see the impact of the change you made in Variation B.
It’s calculated with this formula:
((Conversion Rate B - Conversion Rate A) / Conversion Rate A) × 100
Example: If Variation A had a conversion rate of 8% and Variation B had 10%, the improvement is:
((10 - 8) / 8) × 100 = 25%
This means Variation B improved conversions by 25% compared to Variation A. If the result is negative, it means Variation B performed worse than A.
This percentage gives you a quick idea of how effective your new version was — and whether it's worth implementing the change permanently.
After calculating your A/B test results, you’ll see a few statistical metrics: the Z-Score, the P-Value, and the Confidence Level. These may sound technical, but they are essential for understanding how reliable your results are.
The Z-Score measures how different your two variations are in terms of conversion rates. It tells you how far the difference is from zero (no difference), in standard deviation units.
A high Z-Score means the difference between A and B is large and unlikely to be due to chance. A low Z-Score means the difference is small and may have occurred randomly.
In simple terms: The higher the Z-Score, the more confident you can be that one version truly performs better than the other.
The P-Value tells you how likely it is that the difference you observed happened by chance. It ranges from 0 to 1.
For example, a P-Value of 0.03 means there is a 3% chance that the difference happened randomly, and a 97% chance that it's a real effect.
Tip: A commonly used threshold for significance is 0.05 (or 5%). If your P-Value is below 0.05, your result is considered statistically significant.
The Confidence Level is the percentage of certainty that the difference in conversion rates is not due to random chance. It’s calculated as:
Confidence = (1 - P-Value) × 100
So, if your P-Value is 0.04, the confidence level is 96%.
In everyday language: If you have a 95% confidence level, you can be 95% sure that one variation performs better than the other.
Here’s a quick guide:
Understanding these metrics helps you make informed decisions and avoid acting on results that might be due to chance.
Once you've calculated your A/B test results, the final step is understanding what they mean for your business. The calculator provides a confidence level that tells you how likely it is that the difference between the two variations is real — not just due to random chance. Here’s how to interpret the outcome based on your confidence level:
If your confidence level is 95% or higher, it means there's strong statistical evidence that one variation outperformed the other. In other words, you can be reasonably sure that the change you tested made a real impact.
What to do:
A confidence level between 90% and 94.9% suggests that one variation may be better, but the evidence isn’t strong enough to be certain. The result is suggestive but not conclusive.
What to do:
Sometimes, even small improvements can be meaningful, so you may still choose to adopt the better-performing variation — but proceed with caution.
If your confidence level is below 90%, the difference between the two versions is not statistically significant. This means the variations likely perform similarly, and the observed difference could just be random noise.
What to do:
Remember: Not all tests will produce a clear winner — and that’s okay. Even tests with no significant difference provide valuable insight about your audience and help guide your next steps.
While using the A/B Test Calculator, you might encounter a few common input errors. Don’t worry — they’re easy to fix. Here’s what to watch out for and how to correct them:
What’s the problem?
You entered a number of conversions that is higher than the number of visitors for one of the variations. This isn’t possible — a visitor must see your page or experience before they can convert, and not everyone will convert.
How to fix it:
Once corrected, the calculator will work as expected.
What’s the problem?
The calculator requires all fields to be filled with valid numbers. If you leave a field blank, enter zero visitors, or type non-numeric values, the calculator won’t be able to process the results.
How to fix it:
If the form still doesn’t work, recheck your data and ensure every input follows the required format.
Tip: If the calculator displays an error message, read it carefully — it’s designed to help you fix the issue quickly and continue with your test.
Yes! If you're testing two different subject lines, layouts, or call-to-actions in an email campaign, you can use this calculator. Just treat the number of recipients as “visitors” and the number of people who clicked or converted as “conversions.”
A conversion is any action you’re measuring as a success — such as signing up, making a purchase, clicking a link, or completing a form. It depends on the goal of your test.
While there's no fixed rule, larger sample sizes generally produce more reliable results. If your confidence level is low, try running the test longer or collecting more data before making decisions.
It’s still a useful outcome! It means the change you tested didn’t make a big impact — or your sample size may be too small. Use the insights to plan a new test with different variations or changes that might have a stronger influence.
This calculator is designed for simple A/B tests (two variations). For tests with multiple versions (A/B/C or more), you'll need a more advanced tool that supports multivariate testing and accounts for multiple comparisons.
Confidence levels help you determine how likely it is that your results are real, not random. Acting on tests with low confidence can lead to misleading conclusions. Understanding confidence helps you make smarter, evidence-based decisions.
No — the calculator only accepts whole numbers for visitors and conversions. Enter raw counts (e.g., 500 visitors, 45 conversions), and it will calculate percentages for you automatically.