Your question is Statistical Significance Under Constraints. 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).
How do you determine statistical significance when dealing with high-variance enterprise metrics where sample sizes are inherently constrained?
Explain how you would define an estimand, choose the randomization and analysis unit, quantify variance, set a practically meaningful MDE, and assess power before running the test. Describe how you would use variance reduction, robust inference, pre-registered stopping rules, and guardrails when a conventional large-sample test is underpowered. Your answer should include a numerical sample-size calculation, an analysis plan, and a ship, iterate, or do-not-ship rule.