##### Scenario VP and PM jointly evaluate how a candidate designs and interprets experiments and defines product metrics when running many concurrent tests under tight timelines. ##### Question You must run roughly 100 experiments but lack the traffic to let each reach the usual 0.05 significance level. How would you adjust the overall alpha (Type-I error) threshold so the launched features are as impactful as possible? After seeing preliminary results, how would you incorporate an adaptive approach—such as a multi-armed bandit—to update decision thresholds over time? For a new feature, define the primary success metric you would track, explain why it matters, and describe how you would guard against metric swamping or gaming. ##### Hints Discuss multiple‐testing corrections (Bonferroni, Holm, FDR), sequential testing, power vs. speed trade-offs, and principled metric selection (north-star, guardrails).