Free A/B Test Calculator

Want to increase content engagement or conversation rates and get better campaign results? Conduct A/B tests to drive successful campaigns with Jotform’s A/B Test Calculator. Measure test results, create reports, and drive more successful campaigns now.



Conversion rate






Significant Result

Variant B’s conversion rate (1.14%) was 14% higher than variant A’s conversion rate (1.00%). You can be 95% confident that variant B will perform better than variant A.

Power: 86.69%

p value: 0.0314

Free A/B Testing Templates

Not sure how to build your first control form? No problem. Choose one of our ready-made templates to get started. Customize it to match your needs, share it with your users, and start collecting the information you need to run your A/B tests.

AB Test Log Form

AB Test Log Form

Market Research Survey

Market Research Survey

Target Audience Grouping Form

Target Audience Grouping Form

Form Builder

Collect Information Easily

Measure KPIs and drive more successful marketing campaigns with Jotform’s A/B Test Calculator. Use our drag-and-drop builder to customize your A/B testing forms, then share those forms via direct link or embed them into your website or online portal.

Form Builder

Jotform Tables

Segment Your Audience

Use Jotform Tables to track and manage user responses in one central location. Assess which version of your campaign or digital asset works the best. Easily identify subgroups, trends, and more to start building more meaningful user relationships.

Jotform Tables

Report Builder

Analyze and Visualize Data

With Jotform Report Builder, you can create professional reports by using data from your A/B testing forms. Customize your reports to show off your brand and share reports with stakeholders and clients.

Report Builder

A/B Testing Calculator FAQ

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  • What is A/B testing?

    A/B testing, sometimes referred to as split testing, is a type of test that contrasts two variations of something to detect which version is preferable. It’s often used for a marketing campaign or something similar. In A/B testing, users are randomly selected to receive one of two options, and statistical analysis is conducted based on their feedback.

  • What are A/B tests used for?

    A/B tests are used in many different industries for different reasons. Marketing and advertising professionals commonly use A/B tests to determine the best option to use in website design, online offers, headlines, emails, and product descriptions.

  • How are A/B tests calculated?

    A/B tests are calculated by comparing the conversion rates of the two different variations against each other to see if one has higher statistical significance than another. Some key factors to consider when calculating your A/B test are z-score, P-value, sample size, confidence interval, and confidence score.

  • How can I understand the success of an A/B test?

    The statistical significance of your A/B test should ideally be 95% and not lower than 90% at the very least. Otherwise, it is not a successful test.

  • How many samples are needed for an A/B test?

    The required sample size depends on the A/B test and what it is measuring. Calculations should be performed to determine the minimum required sample size for the given A/B test.

  • What is statistical significance in A/B testing?

    In A/B testing, statistical significance ensures that the difference between the control and test groups of the experiment is not the result of chance or error. The higher the statistical significance, the more likely that the difference between your groups is accurate. In short, statistical significance is a measure of how probable it is that your null hypothesis is true.

  • What is the statistical significance formula?

    The statistical significance formula is:

    χ2= ∑(O−E)2/E

  • What is the z-score?

    The z-score is a statistical measurement that indicates how much a given value differs from the standard deviation. In simple terms, the z-score shows you how far your tested data point is from your mean.

  • What is P-value?

    The P-value is the probability that there is no difference between your test groups and that the indicated improvement is completely by chance.

  • What is a null hypothesis?

    The null hypothesis refers to the baseline assumption that there is no connection between two sets of data.

  • What is the “statistical power” of a test?

    The statistical power of a test is the probability that your test will find a statistical difference between the test groups, effectively deviating from the null hypothesis.

  • What is the difference between one- and two-sided tests?

    A one-sided test can determine if one (and only one) mean is greater or less than another mean. A two-sided test can determine if two means are different from each other.

  • What is an A/B confidence score?

    An A/B confidence score grades the validity of a successful A/B test. Generally, a score above 95% is ideal.

  • How can I run an A/B test?

    You can run an A/B test by following the steps below:

    • Enter the number of visitors and conversions for your A variant.
    • Enter the number of visitors and conversions for your B variant.
    • Determine your test’s confidence interval.
    • Calculate, analyze, and share your results.
  • How can I create an A/B test?

    You can create your own A/B test with Jotform by starting from scratch or choosing one of our ready-made templates. Create and customize different A/B test forms to test your options and see which is more effective in the eyes of your audience.

  • Can I customize my A/B test?

    Yes, you can customize your A/B test forms with Jotform’s drag-and-drop builder. Add or remove form fields, change up copy, upload branding assets or images, pick fonts and colors, and more depending on your needs.