A/B testing compares two versions of a page, ad, or email by splitting traffic between them and measuring which performs better. It replaces opinion with evidence about what actually works. Reliable results require enough traffic and a disciplined approach to what counts as a genuine difference.

A/B testing is powerful and routinely misused by businesses without the traffic to support it. If you have a few hundred visitors a month, you cannot detect a real difference, and acting on an apparent winner is just acting on noise. Testing works when volume is sufficient. Below that threshold, better judgment beats false science.

Example:

Declaring a winner after 40 visitors and three conversions is noise. The same test across thousands of visitors can produce a result worth acting on.

How much traffic does A/B testing need?

Enough for the difference to be statistically detectable. With low traffic, apparent winners are usually random variation rather than genuine improvement.

What is the most common A/B testing mistake?

Stopping a test early and acting on a result that has not reached significance, which means acting on noise rather than evidence.