Why Your A/B Test Winner Might Just Be Random Noise

A/B test results can often be misleading, where random noise is mistaken for a significant outcome, particularly in small datasets. Using a fictional example of a football coach, the piece illustrates how an apparent 8% performance improvement could be a statistical fluke rather than a true effect. To avoid being fooled by randomness, it is crucial to implement rigorous experimental design, including pre-defining hypotheses, ensuring fair randomization, and applying appropriate statistical corrections. These principles are essential for validating results and apply broadly across domains like marketing and healthcare, ensuring that a one-off spike is not celebrated as a real breakthrough.