How A/B Testing Goes Wrong: The Most Common Statistical Pitfalls

A/B testing looks simple — show two versions, measure the difference, pick the winner. But peeking at results, multiple comparisons, and ignored novelty effects cause teams to ship changes that don't actually work. Understanding these traps separates real data scientists from dashboard-watchers.