12-24-2025, 12:29 PM
I'm a data analyst for a marketing team, and we're running an A/B test on our website's new checkout page design, but I'm concerned our hypothesis testing approach is flawed because we didn't properly calculate the required sample size upfront and might be stopping the test too early based on what looks like a significant result. We hypothesized the new design would increase conversion by at least 5%, and after a week we're seeing a 7% lift, but the traffic is relatively low and I suspect we're being misled by random variation. For others who design and interpret these tests regularly, what's your process for ensuring statistical rigor from start to finish? How do you determine the minimum detectable effect and duration before launching, and what are the key metrics you monitor to decide if a result is truly valid or if you need to let the test run longer to achieve proper power?