A/B Testing

What is A/B testing?

In an A/B test you randomly split your traffic between two versions. Version A is usually what you have today, version B contains one deliberate change. Both run at the same time, and you measure which one improves a metric you chose in advance, such as clicks, sign-ups or purchases.

Because both groups see the product under the same conditions at the same time, you can attribute a difference to your change rather than to seasonality, campaigns or chance. Testing more than two versions or several elements at once is called A/B/n or multivariate testing.

Why A/B testing matters

Opinions in a meeting are easy to come by. Real behavior is more reliable. An A/B test replaces gut feeling with evidence. In an online store, you find out whether a new product page actually drives more revenue. In a SaaS product, you see whether a reworked onboarding step gets more users to activate. It also protects you from changes that look great but quietly hurt your numbers.

How to use A/B testing in practice

• Start with a hypothesis: What are you changing, what effect do you expect and why? Strong hypotheses come from usability tests and analytics.
• Pick one primary metric before the test starts and don't switch it afterwards.
• Plan for enough traffic and time. Calculate the required sample size up front and run the test for full weeks so weekday patterns even out.
• Change one thing per variant so you can clearly attribute the result.
• Document every result, including tests without a winner. They tell you what doesn't move your users.

Common mistakes

The most common mistake is stopping a test early because one variant is ahead. Short-term swings are normal and often fade. Running tests without a clear hypothesis is just as risky. You learn that B won, but not why, and have nothing to build on.