A/B Testing

An A/B test shows two versions of a page or feature to two random user groups at the same time, to learn which performs better.

Because both groups are random and concurrent, any outcome difference can be confidently attributed to the change itself, not seasonality or campaigns. It requires enough users and patience to reach statistical significance — stopping early is the most common mistake.