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A/B Testing

A controlled experiment that shows two versions of the same screen to comparable groups of users to find out which one performs better.

Growth & Metrics

A/B testing splits live traffic between a control and one variant, then measures a single pre-agreed metric. The point is not to collect opinions but to remove them: whichever version moves the metric wins, regardless of who argued for it. Anything more than one variable at a time and you can no longer say what caused the change.

Most A/B tests fail for arithmetic reasons rather than design ones. Low-traffic products cannot reach significance on a button colour in any reasonable timeframe, so the honest choice is to test structural changes — pricing layout, onboarding order, form length — or to skip testing and use usability testing instead. Testing is a tool for settling expensive disagreements, not for validating every decision.

In practice

A fintech dashboard team disagreed about whether to ask for KYC documents before or after the first deposit. Before: 41% completed signup. After moving KYC behind the deposit step: 58%. The catch — support tickets rose, because some users deposited and then discovered they could not withdraw yet.

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You may ask

Frequently Asked Questions

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

Enough to detect the effect you care about. Detecting a 1% lift needs tens of thousands of sessions per variant; detecting a 20% lift on a signup flow may need only a few hundred. If you cannot estimate the effect size, you are not ready to test.

Can I A/B test a redesign?

You can, but a full redesign changes dozens of variables at once, so a win tells you the new version is better without telling you why. Test a redesign as a whole to decide whether to ship it, then test individual elements afterwards to learn.

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