A/B Testing

Definition

A/B testing or split testing is a method of comparing two versions of a web page or other user experience to determine which one performs better. It's a way to test changes to your webpage against the current design and determine which one produces better results. It's done by showing the two variants, A and B, to two similar visitor groups and comparing the engagement or conversion rate to determine which version is more effective.

What A/B Testing means in A/B testing

In practical experimentation, A/B Testing helps define how a test is structured and how results should be interpreted. Teams use it to align marketers, designers, analysts, and developers before an experiment goes live.

Why A/B Testing matters

A/B Testing matters because it affects how an experiment is designed, launched, interpreted, or acted on. Clear definitions help teams avoid comparing the wrong audiences, metrics, or variants.

Example of A/B Testing

For example, when launching a homepage experiment, the team can use A/B Testing to clarify the audience, variant setup, metric, or analysis method before traffic is split between experiences.

How to use A/B Testing

Use A/B Testing during experiment planning so everyone agrees on setup, measurement, and decision criteria. Document it before launch, then refer back to it when analyzing the final result.

Common mistake

A common mistake is using A/B Testing loosely without documenting the exact audience, metric, or variant definition. That makes test results harder to explain and easier to misinterpret later.

Related A/B testing terms

• Analysis
• Control
• Control Group
• Metrics

FAQ

What is A/B testing?

A/B testing or split testing is a method of comparing two versions of a web page or other user experience to determine which one performs better. It's a way to test changes to your webpage against the current design and determine which one produces better results.

Why does A/B testing matter for experiments?

A/B Testing matters because it affects how an experiment is designed, launched, interpreted, or acted on. Clear definitions help teams avoid comparing the wrong audiences, metrics, or variants.

How should teams use A/B testing in an experiment?

Use A/B Testing during experiment planning so everyone agrees on setup, measurement, and decision criteria. Document it before launch, then refer back to it when analyzing the final result.