A/B Testing
A/B testing is a controlled experiment that compares two versions of something by randomly splitting users between them to see which performs better on a chosen metric.
In an A/B test, users are randomly assigned to variant A (often the current version) or variant B (the change), and you measure how each group behaves against a specific goal, such as sign-ups, clicks, or conversions. Random assignment isolates the variable you changed, so a statistically significant difference can reasonably be attributed to that change rather than chance.
A/B testing matters because it replaces opinion with evidence. Instead of debating which headline, layout, or flow is better, you let real user behavior decide. Done well, with a clear hypothesis and an adequate sample size, it reduces the risk of shipping changes that feel like improvements but actually hurt the numbers.
Experiments and feedback reinforce each other. Qualitative feedback tells you what to test and why users struggle; an A/B test tells you whether your proposed fix actually moves the needle. Pairing the two means you act on feedback with confidence instead of guessing.