An A/B test compares two variants of a product, page, or feature with real users to determine which one performs better based on data. Rather than debating internally whether variant A or B is superior, the question is delegated to reality. This makes A/B testing one of the most direct tools for replacing opinions with evidence.
In practice, one half of users sees variant A (for example, the existing landing page) while the other half sees variant B (with a modified headline or different call-to-action). A clearly defined metric is measured, such as click-through rate, conversion, or time on page. The critical rule is that only one variable is changed at a time, since otherwise it remains unclear what caused the difference. With a sufficient sample size, the test delivers a reliable result.
A/B testing originates from statistical experimental design and is now standard in digital product development. An important distinction: an A/B test answers the question “What works better?” but not “Why?” Understanding the underlying causes requires complementary qualitative methods.
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