Test whether US uploads more videos

You want to evaluate the hypothesis: > “US members upload more videos than non‑US members.” You have: ### Table: `video_posts` | column | type | |---|---| | post_date | DATE | | memberid | BIGINT | | video_length | INT | ### Table: `members` | column | type | |---|---| | memberid | BIGINT | | country | STRING | | join_date | DATE | Assume you are analyzing a fixed window (e.g., **2018-12-01 to 2018-12-31**, inclusive), but your approach should generalize. **Task:** Propose a rigorous analysis plan to answer the question. Your plan must include: 1. **Metric definitions** (at least one primary metric and at least two diagnostics/guardrails). For example, decide whether to compare: - total uploads, - uploads per member, - uploads per active member, - uploads per member-day (exposure-adjusted), etc. 2. How you will handle key confounders such as: - different numbers of members in each group, - different member tenure (new vs old accounts), - different activity levels and seasonality. 3. A statistical approach to quantify uncertainty (e.g., confidence intervals, hypothesis tests, regression model), and when you would prefer each. 4. At least one **failure mode** (e.g., Simpson’s paradox from tenure differences) and how you’d detect it. You may include example SQL/Python pseudocode to compute the metrics, but the focus is on correct experimental/observational analysis design and interpretation.