A/B testing, also known as split testing, is a method used to compare different versions of a webpage, app feature, or marketing element to determine which one performs better by analyzing actual user behavior and statistical evidence. It shifts decision-making from speculation to data-driven insights, helping businesses increase conversions, validate ideas, and foster a culture of continuous improvement. This approach is increasingly crucial as traffic acquisition costs rise and traditional strategies face uncertainty. A/B testing allows for incremental changes to reduce risks, resolve internal debates through data, and uncover surprising insights, as evidenced by companies like Microsoft and Netflix. The process involves forming hypotheses, designing tests, calculating sample sizes, and analyzing results while avoiding common pitfalls like early peeking or ignoring segment differences. Advanced strategies include sequential and bandit testing, personalization, and full-funnel testing, all aimed at building a robust testing culture that embraces evidence-based decision-making. As technology evolves, A/B testing continues to integrate AI, real-time personalization, and privacy-first methods to enhance its effectiveness.