A/B Testing

What is A/B Testing?

A/B testing means splitting traffic between two variants — a control and a challenger — and measuring which one produces better results. It's the foundation of data-driven experimentation and the most widely used method for making decisions based on evidence rather than opinions.

What it means in practice

In practice, A/B testing is about more than the tool. It's about formulating a hypothesis, defining success metrics, running the test long enough, and interpreting the result correctly. Many teams run tests without a clear question. That makes the test meaningless regardless of outcome. A good A/B test always starts with "we believe X will lead to Y, because Z." Without that structure, you're testing — but you're not learning.

Why it matters

A/B testing reduces the risk in decisions. Instead of shipping changes based on gut feeling, you know what actually works before you roll it out. It protects against bad decisions and accelerates the good ones. In organizations that test systematically, fewer decisions are made based on HIPPO (Highest Paid Person's Opinion).

Common mistakes

• Stopping the test too early — results don't have time to reach statistical significance
• Testing too many things at once without clear prioritization
• No documentation — insights get lost between tests