A/B Testing
A method of comparing two versions of a product or feature to determine which performs better with users.
How A/B Testing Works
A/B testing, sometimes called split testing, is a controlled experiment where two variants of a product element are shown to different user segments simultaneously. Variant A is typically the existing version (the control), while Variant B contains a specific change (the treatment). By measuring how each group interacts with their respective version, teams can make data-driven decisions about which design, copy, or feature performs better against a predefined metric such as click-through rate, conversion rate, or task completion time.
The process starts with forming a hypothesis, for example, “Changing the sign-up button from blue to green will increase registrations.” Traffic is then randomly split between the two variants. Statistical analysis determines whether the observed difference in performance is significant or simply due to chance. Most teams use a confidence level of 95 percent before declaring a winner. A/B testing is widely used during beta testing programs because you already have an engaged user base providing real-world interaction data.
Why A/B Testing Matters
Product decisions driven by opinions or assumptions often miss the mark. A/B testing replaces guesswork with evidence. Even small improvements discovered through split tests can compound over time, leading to meaningfully better user experiences and business outcomes. When combined with feature flags, teams can run experiments safely, rolling back changes instantly if a variant underperforms or causes unexpected issues.
A/B testing is also a natural complement to qualitative feedback gathered from beta testers. While usability testing tells you why users struggle, A/B testing tells you which solution fixes the problem more effectively. Together, they form a powerful feedback loop. If you are running a beta program, consider reading Running a Beta Program for tips on structuring tests alongside user experiments.
Best Practices
To get reliable results, change only one variable at a time. Testing multiple changes simultaneously makes it impossible to isolate which modification drove the outcome. Ensure your sample size is large enough to reach statistical significance; ending an experiment too early can lead to false conclusions. Define your success metric before the test begins, not after, to avoid cherry-picking favorable data. Finally, document every experiment and its results so the team builds a shared knowledge base over time. For a broader look at metrics you should track, see Beta Testing Metrics.