What is A/B testing and when should it be used?

A/B testing is a statistical method used to evaluate the effectiveness of a new product feature or design by comparing it to a baseline (usually the current version). Users are randomly assigned to either the control group (which sees the existing version) or the treatment group (which experiences the new change). The key goal is to measure how the new version impacts a pre-defined success metric such as engagement, conversion rate, or time spent on the platform. Properly run A/B tests enable product teams to make causal inferences about the effect of changes.

However, A/B testing should only be used under certain conditions.

1. The infrastructure must reliably log user interactions and metric data.
2. The platform should have sufficient traffic to ensure statistical significance within a reasonable time. Third, the change being tested must be isolatable; if the change is visible to everyone or causes network spillover (e.g., a new layout covered widely in media), random assignment will be compromised.
3. The metric being tested should show measurable results within the duration of the test. If long-term outcomes such as retention or lifetime value are the goal, A/B testing might not be feasible without proxies. In such cases, use machine learning models or retrospective analysis.