Your question is Common Pitfalls in Experiment Results. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You work on a digital product team that runs frequent A/B tests and often sees conflicting reads from the same experiment depending on when the results are checked. Some launches look strong at first, then flatten out, while others show wins in the primary metric but raise concerns in related metrics.
What are the common pitfalls you watch for when interpreting experiment results, and how do they change your confidence in whether a test should ship? Focus on the issues that can make a result look better or worse than it really is.