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A/B testing is a method in data science used to compare two versions of a variable to determine which one performs better. It involves randomly dividing a sample into two groups: A (control) and B (variant). By comparing metrics such as conversion rates, click-through rates, or user engagement, data scientists can identify which version yields better results. This technique is widely used in marketing, product development, and UX design to optimize decisions based on data-driven insights. A/B testing helps in understanding user preferences and making informed improvements to products or services.

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