A/B Test

Technical terms
About 1 min read

An experimental method of exposing two or more versions simultaneously to statistically compare which one is more effective.

Also known as
A/B TestA/B TestingSplit TestSplit Test

Detailed explanation

A/B testing is an experimental method where two or more versions (A and B) are randomly shown to users to statistically compare which version performs better on key metrics. It is used to make evidence-based improvements to elements like website designs, email subject lines, ad copy, pricing policies, and button text. To obtain reliable results, a sufficient sample size and verification of statistical significance are required. Representative tools include Optimizely and VWO, and using Multi-Armed Bandit algorithms can optimize testing by automatically allocating more traffic to better-performing variants.

Why it matters in tool selection

Using a tool with A/B testing features allows you to determine improvements based on data rather than assumptions. However, if the sample size is small or statistical significance is not calculated, it is easy to draw incorrect conclusions. The key to obtaining reliable results is whether the tool supports significance calculations, sample size guidance, and simultaneous multi-experiment management.

Key checkpoints for tool selection

  • Does it provide guidance on statistical significance and the required sample size?
  • Can it run multiple experiments simultaneously and manage conflicts?
  • Can it track actual goal metrics such as conversions or revenue?
  • Does it support automatic traffic allocation, such as Multi-Armed Bandit?

Real-world use cases

For example, you can randomly show visitors two versions of a sign-up button, 'Start for Free' and 'Try Now,' and compare sign-up conversion rates over two weeks. Once samples accumulate and a statistically significant difference is verified, you adopt the better-performing copy, iterating on evidence-based improvements.

Related terms

Data AnalysisSEOPersonalization