A/B testing is a controlled experimental method based on statistical principles that validates hypotheses and optimizes decisions by randomly assigning users to different versions and comparing their performance. This method is widely applied in product iteration, marketing strategy optimization, and user experience improvement, helping teams make more scientific decisions in a data-driven way rather than relying on subjective judgment. Test results are typically evaluated based on key indicators like conversion rate, click-through rate, and retention rate, with significance testing ensuring the reliability of conclusions.
