A/B Test
What is an A/B Test?
An A/B test (also known as split testing) is a controlled experiment that compares two versions of a webpage, interface, or individual design component to find which one drives better results. Version A stays unchanged as the control, and Version B introduces one modification, such as a different headline, button placement, or navigation pattern. Both versions run simultaneously, with traffic split randomly between them and performance measured against a predefined goal.
What are the benefits of A/B testing?
Data over debate — when two stakeholders disagree on a change, test results settle it without politics.
Precise attribution — testing one variable at a time lets you trace any performance shift directly to that specific modification.
Reduced risk — validating changes before full rollout prevents shipping something that quietly hurts performance.
Live user feedback — results come from your actual audience, not assumptions about how they behave.
Lower cost of mistakes — catching a poor design decision at test stage is significantly cheaper than fixing it post-launch.
Scalability — the same method applies whether you're testing a button label or an entire onboarding flow.
Why is it worth running?
The further a design decision is from reversible, the more it benefits from testing first. Changing a CTA label is low risk. Restructuring navigation, redesigning an onboarding flow, or rolling out interface changes across a legacy platform — those carry consequences that show up in retention and conversion long after launch.
That value compounds in situations where the cost of being wrong is high. After a UX audit has identified what's broken and a fix needs validating, or mid-redesign, when teams need evidence before changes go wide, it's a pressure valve against assumptions.
Remember, A/B tests answer which performed better, not why. Pair the results with usability research if you need the full picture.
When Shouldn't You Run an A/B Test?
Not every design decision belongs in a test. Four situations where running one is likely to waste time or produce misleading data.
Traffic volume is too low. Without enough users moving through both versions, results won't reach statistical significance.
The hypothesis is vague. "This might perform better" produces data you can't act on. A useful hypothesis names the specific change, the expected outcome, and the reasoning behind it.
Multiple variables changed at once. If versions A and B differ in three places, there's no way to isolate which change drove the result.
The question is qualitative. When the goal is understanding why users behave a certain way, usability testing or interviews will get there faster.
How do you conduct an A/B test?
Form a specific hypothesis. Base it on user data, analytics, or audit findings. Name the change, the metric it should affect, and the expected direction.
Build two versions. Keep everything identical except for one modification; this is what makes split testing results interpretable.
Split traffic randomly. Users should be assigned to versions automatically, not by time of day or device type.
Run until statistical significance is reached. Stopping early because initial numbers look promising is one of the most reliable ways to get false results.
Act on the findings. A clear winner gets implemented. Inconclusive results point back to the hypothesis.
Which metrics should you track?
These five cover the most common A/B test scenarios.
Conversion rate: User completion of the target action
Bounce rate: Attention retention past the first interaction
Click-through rate (CTR): Interaction rate on buttons, links, or CTAs
Task completion rate: How successfully users accomplish a defined goal
Session duration: Depth of engagement with the page or product