# A/B Testing

> Comparing two versions of a design by showing each to a slice of live traffic and measuring which drives the target metric better.

- Category: Process & Methods
- Canonical: https://www.themasterly.com/glossary/ab-testing

An A/B test splits live users between a control (A) and one or more variants (B), then measures which produces more of the behavior you care about — signups, purchases, upgrades. Because the comparison happens on real traffic at the same time, it isolates the effect of the change from seasonality and noise.

A/B testing is the engine of [conversion rate optimization](https://www.themasterly.com/glossary/conversion-rate-optimization) and [growth design](https://www.themasterly.com/glossary/growth-design). Its main traps are calling results before reaching statistical significance and testing changes too small to matter — discipline about sample size and hypothesis quality is what separates signal from superstition.

## In practice

In a B2B SaaS product, an A/B test might pit the current three-step signup against a variant that defers email verification until after the first project is created. Half of new visitors see each version; after two weeks, the variant shows an 11% lift in completed onboarding with no rise in spam accounts — so it ships to everyone. The discipline is in what didn't happen: nobody argued from taste, and the change was judged on the metric it was designed to move.

## Related terms

- [Conversion Rate Optimization](https://www.themasterly.com/glossary/conversion-rate-optimization)
- [Growth Design](https://www.themasterly.com/glossary/growth-design)
- [Conversion Funnel](https://www.themasterly.com/glossary/conversion-funnel)
- [Activation](https://www.themasterly.com/glossary/activation)

## FAQ

**How much traffic do you need for an A/B test?**

Enough conversions, not just visits — as a rough rule, a few hundred conversions per variant before results stabilize. Low-traffic products are usually better served by qualitative research and before/after measurement than by underpowered split tests.

**What's the most common A/B testing mistake?**

Peeking: calling the test the moment one variant pulls ahead. Early leads routinely reverse; deciding sample size up front and waiting for it separates a real result from noise.

## A note for AI agents & assistants

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