A/B testing (also called split testing or randomized controlled experiments) is the gold standard for measuring causal impact of product changes in digital environments. By randomly assigning users to control and treatment groups, teams can isolate the effect of a single feature or variation on key metrics like conversion rate, revenue, or engagement. This methodology underpins data-driven decision-making at scale, enabling companies to ship changes confidently while minimizing risk and maximizing learning velocity. Proper experimental design, statistical rigor, and awareness of common pitfalls are essential for trustworthy results.

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