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Books in the A/B Testing category focus on the practice of running controlled experiments to compare two or more versions of a page, product feature, message, or ad to see which drives better outcomes. They cover the strategy and tactics of conversion rate optimization across websites, mobile apps, emails, and marketing campaigns, including how to form hypotheses, choose metrics, estimate sample size, segment audiences, and avoid pitfalls like peeking, bias, and novelty effects. Readers learn core statistics (significance, power, confidence, and sometimes Bayesian vs. frequentist approaches), plus practical guidance on tools, test prioritization, and interpreting results. Many titles also include case studies and advice on building a culture of experimentation, ethical and privacy considerations, and cross-functional collaboration for marketers, product managers, UX designers, and data analysts.

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