Product teams ship features hoping users will love them—but hope isn't a strategy. Without A/B testing, decisions rely on gut feelings or HiPPOs (Highest Paid Person's Opinion). Blind guesses result in shipping features that lower sign-ups, frustrate users, or cost months of wasted development time. A/B testing transforms guesswork into evidence, yet many teams struggle with implementation complexity, improper randomization, or flawed analysis. Understanding the basics behind experimentation is critical for building products users actually prefer.

This talk breaks down A/B testing from theory to practice with A/B Testing Basics, Bucket Selection Algorithms, and Allocation Rules: Targeting specific segments and a demo. This isn't another slide deck about A/B testing theory—it's an interactive experience where the audience becomes the experiment. By the end, attendees will understand not just what A/B testing is, but how it feels to be a data point.