A/B testing is a widely used technique in the field of data science for evaluating the effectiveness and performance of models and strategies in real-world scenarios. It provides a controlled framework for comparing two versions of a model, algorithm, or system component to determine which one performs better according to predefined metrics. A/B testing enables data scientists to make data-driven decisions and validate hypotheses with statistical rigor, ensuring that changes made to models or...