A/B Testing and Experimentation in Data Science

A/B testing is a key method in data science for comparing two versions of a product or feature to determine which performs better. By randomly splitting users into two groups (A and B), where one group sees the original version and the other sees the new version, data scientists can measure differences in outcomes like click rates or sales. This experimentation helps make data-driven decisions, improving products and user experiences. It’s a simple yet powerful way to test hypotheses and optimize performance in real-world settings.

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