Why A/B Testing Fails Businesses and When to Use Smarter Alternatives

Standard fixed-horizon A/B tests split traffic evenly and wait for a predetermined sample size before drawing conclusions, but this approach assumes test groups are independent and ignores revenue lost during the experiment. A multi-armed bandit method addresses this by continuously shifting more traffic toward the better-performing variant while still exploring alternatives, balancing the exploration-exploitation tradeoff. The concept dates to World War II research and was formally studied by mathematician Herbert Robbins in 1952, with practical strategies like epsilon-greedy, UCB, and Thompson sampling still used in production today. In a hypothetical pricing test where one variant shows a 15% conversion rate versus 10% after just one week, a bandit approach would redirect most traffic to the winning variant rather than maintaining a rigid 50/50 split for five weeks. The key tradeoff is that bandits optimize for revenue during the test but typically require more time to reach the same statistical confidence that a fixed-horizon test provides upfront.