5 Myths That Keep You From Running More Experiments

Imagine you’re gearing up for a big experiment—maybe it’s a new feature, a pricing change, or a redesign. You’re excited, but then someone from leadership says, “Wait, won’t overlapping experiments mess up the results?” A data scientist chimes in, “CUPED will solve our sample size problem, right?” Meanwhile, your team debates whether a holdout group will really help measure long-term impact. Sound familiar? These are just a few of the myths that keep teams from running more experiments, slowing down innovation and decision-making.

In this webinar, Pritul Patel, an experienced data scientist and experimentation platform product manager (Apple, Peacock TV, eBay, Yahoo), will tackle these myths head-on using real-world examples, intuitive math, and visual stats. He’ll explain why ARPU isn’t the north star metric you think it is, why copying your competitor’s CRO tactics won’t guarantee success, and why common interpretations of AA tests often lead to the wrong conclusions. If you’ve ever hesitated to run an experiment because of these concerns, this session will give you the confidence to test more, test smarter, and move faster.