This sounds clean on the surface but the randomization piece is where it gets tricky.
Start by defining the hypothesis and success metrics, then explain the randomization unit (e.g., user-level) and how to ensure proper assignment. Walk through the experiment setup, including control/treatment groups, guardrail metrics, and analysis plan, while addressing potential pitfalls like network effects and novelty effects.
Pro tip: At Meta, where social interactions are key, emphasize how you'd handle interference between users (e.g., if a user sees content from a non-followed person, that person's metrics might be affected). Mention techniques like cluster randomization or ego-network randomization to mitigate this.
Clearly state the hypothesis (e.g., showing non-followed content increases engagement) and select primary metrics (e.g., time spent, likes, comments) and guardrail metrics (e.g., user reports, unfollows).
Decide on the randomization unit (e.g., user-level) and explain how to randomly assign users to control (no non-followed content) and treatment (with non-followed content) groups, ensuring balanced groups.
Discuss potential interference (e.g., a user's behavior affecting others) and propose solutions like cluster randomization (randomizing by social clusters) or ego-network randomization.
Calculate required sample size based on expected effect size, power, and significance level; set experiment duration to capture enough data and account for novelty effects.
Plan for statistical analysis (e.g., t-test, regression), check for novelty/primacy effects, and validate that randomization worked (e.g., AA test, covariate balance).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.