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.

Visitors

Conversions

Conversion rate

A

1.00%

B

1.14%

Hypothesis

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

Market Research Survey

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

  • 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?

  • How are A/B tests calculated?

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

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

  • What is statistical significance in A/B testing?

  • What is the statistical significance formula?

  • What is the z-score?

  • What is P-value?

  • What is a null hypothesis?

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

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

  • What is an A/B confidence score?

  • How can I run an A/B test?

  • How can I create an A/B test?

  • Can I customize my A/B test?