Your question is Diagnose Sample Ratio Mismatch Test. 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 are evaluating a new onboarding flow in a consumer financial product and have launched a 50/50 A/B test on the signup entry experience. The team believes the new flow will increase completed signup rate by reducing friction in the first session. After three days, the experiment dashboard shows materially more users in treatment than control, even though the allocation was configured evenly. Before interpreting any lift, you need to determine whether this is sample ratio mismatch and how it affects the validity of the experiment.
How would you design and analyze this experiment so you can detect and diagnose sample ratio mismatch early, quantify whether the test is still trustworthy, and decide whether to continue, restart, or discard the results?