Why the uplift in A/B tests often differs from real-world results

A/B tests often show promising results that do not materialize post-launch, a discrepancy that can be attributed to factors such as human bias, false positives, sequential testing, novelty effects, and issues with external validity. Human biases, like confirmation bias, can skew analysis and interpretation, while false positives can mislead stakeholders about a feature's effectiveness. Sequential testing can overstate effect sizes, and the novelty effect may cause temporary spikes in user engagement that do not persist. Additionally, the limited exposure of tests and real-world complexities can cause significant differences between test outcomes and actual performance. Strategies such as repeated tests, lowering significance levels, using holdout groups, maintaining skepticism, conducting blind analyses, involving peer reviews, and checking effect sizes over time can help mitigate these discrepancies. By understanding and addressing these factors, teams can better align test results with real-world performance, improving decision-making and leading to more successful product launches.