A/B testing is a method of comparing two or more versions of a web page or user experience to determine which performs better. Users are randomly shown different variations, and statistical analysis is used to evaluate performance against a defined conversion goal such as clicks or signups.

How does this A/B testing calculator work?

This A/B testing calculator determines whether the difference in performance between a control (A) and variation (B) is statistically significant. Users input sample size and conversions for each variation, select a one-tailed or two-tailed test, and receive significance results at a 95% confidence level.

What is the difference between a 1-tailed and a 2-tailed test?

A 1-tailed test is used when there is a specific directional hypothesis, such as expecting one variation to outperform the other. A 2-tailed test is used when no directional assumption is made and evaluates whether there is a significant difference in either direction. The test type must be chosen before running the experiment.

What factors should be considered when running an A/B test?

Key A/B testing factors include sample size, randomization, test duration, accurate measurement, user segmentation, and selecting an appropriate confidence level such as 95% or 99% to determine statistical significance.

A/B Testing Calculator

Calculate statistical significance and conversion lift for your optimization experiments.

Experiment Configuration

Variation Name

Visitors

Conversions

Variation Name

Visitors

Conversions

Real-time Analysis

A/B Testing FAQ

Everything you need to know about A/B testing and statistical significance

A/B testing is a method of comparing two or more versions of a web page or user experience to determine which performs better. Users are randomly shown variations, and statistical analysis is used to evaluate performance against a conversion goal such as clicks or signups.

This A/B testing calculator helps determine whether the difference in performance between a control (A) and a variation (B) is statistically significant. You enter sample sizes and conversions for each variant, choose a one-tailed or two-tailed test, and receive results at a 95% confidence level.

A 1-tailed test is used when you have a specific directional hypothesis, such as expecting one variant to outperform another. A 2-tailed test is used when you want to detect any significant difference, regardless of direction. The test type should be chosen before running the experiment.

Important factors include choosing an appropriate sample size, randomizing users, running the test long enough, accurately measuring conversions, considering user segments, and selecting a confidence level such as 95% to determine statistical significance.

A/B test results should be documented with the test objective, hypothesis, experiment design, sample size, duration, conversion metrics, key findings, and recommendations. Clear documentation helps teams reuse learnings and make informed decisions.

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