An A/B test compares variants by assigning eligible participants to different experiences and measuring a planned outcome. It can estimate the effect of a change when the experiment is designed and run properly. It does not eliminate uncertainty or explain every reason behind the result.
State the question and metric
For a learning page, you might ask whether a clearer copy-control label increases successful copying. Define what counts as success before starting, and keep failures and abandonment visible too. More clicks are not automatically better learning.
Keep the comparison fair
Use random assignment and consistent exposure. Define the sample, duration and analysis approach in advance. Check that assignment and event recording work. A broken event in one variant can make an apparent improvement meaningless.
Interpret the uncertainty
A difference in observed rates can arise from noise. Confidence intervals and an appropriate analysis help assess the estimate; statistical significance does not prove practical importance or rule out every mistake. Avoid repeatedly checking an ordinary fixed-horizon test and stopping at the first favorable result.
Exercise: draft a hypothesis, a primary metric and one metric that would reveal harm. Decide what an inconclusive result would mean before collecting data.
A simple experiment changes one coherent thing, making the result easier to interpret. More complex tests are possible but need suitable design. Keep accessibility requirements in every variant. Use usability testing when you need to understand where and why people struggle.
Sources
Reference 1 provide the technical details and current compatibility information.