How do I avoid false positives in testing?

As a Shopify store owner running multiple conversion optimization tests, I've learned the hard way that false positives can completely derail your growth strategy. Last quarter, I thought I'd discovered this amazing new checkout design that would skyrocket our conversion rates. I ran an A/B test, saw what looked like a significant improvement, and immediately rolled out the changes—only to discover weeks later that the 'improvement' was just statistical noise. It was frustrating and cost us potential revenue. The challenge isn't just about running tests; it's about running them rigorously and understanding the difference between real meaningful improvements and random fluctuations. My team started digging deeper, realizing we needed more sophisticated approaches to validate our hypotheses. We needed a systematic way to ensure that when we see a promising result, it's genuinely representative of a real trend and not just a random spike. This meant understanding statistical significance, sample sizes, and implementing more robust testing protocols. The stakes are high—making the wrong decision based on false positives can lead to wasted development resources, misguided strategy, and potentially decreased store performance.