Why Most A/B Tests Are Lying to You

A/B tests frequently yield unreliable results due to four common statistical mistakes. The 'peeking problem,' or checking results before a predetermined sample size is reached, significantly inflates the false positive rate. Underpowered tests with small samples often exaggerate the true effect size, a phenomenon known as the winner's curse. Testing multiple metrics simultaneously also increases the chance of finding a false positive, requiring statistical corrections. Finally, teams often fail to distinguish between statistical significance and practical significance, shipping changes that are real but too small to matter.