What Is A/B Testing? Definition, Examples & Best Practices

A/B Testing A/B testing (also called split testing) is a controlled experiment where two variants of a product experience are shown to different user groups simultaneously. The variant that performs better on a predefined metric wins. A/B testing removes opinion from product decisions by letting user behavior decide.

Why a/b testing matters

Without A/B testing, product decisions are based on intuition, stakeholder opinions, or the loudest voice in the room. A/B testing provides statistical evidence for what works. It reduces the risk of shipping changes that hurt key metrics and builds a culture of evidence-based decision-making.

How it works

Define a hypothesis and a primary metric (e.g., "changing the CTA copy from Sign Up to Get Started will increase conversion by 10%"). Split traffic randomly between control (A) and variant (B). Run the test until you reach statistical significance (typically 95% confidence). Analyze results and ship the winner.

Common mistakes

• Ending tests too early before reaching statistical significance

• Testing too many variables at once (use multivariate testing for that)

• Not defining a primary metric before starting

• Ignoring secondary metrics that may show negative effects

Related terms

• North Star Metric
• Retention Rate
• Key Performance Indicator
• Feature Toggle