Avoiding the Common Pitfalls in AB Testing

A/B testing is powerful but easy to mess up. As a data scientist, I've seen the biggest issues come from small sample sizes, ignoring the statistical significance and focusing only on statistical significance without thinking about the practical significance of your results. What are the common A/B testing mistakes you've seen, and how are you planning to avoid them?