This felt manageable at first and then immediately got slippery.
Start by clarifying the product goal and user problem, then frame the feature as a hypothesis about increasing engagement. Define primary success metrics (e.g., reply rate) and guardrail metrics (e.g., spam reports), and outline a randomized controlled experiment with proper power analysis and duration. Conclude with how you'd interpret results and make a ship/no-ship decision.
Pro tip: Emphasize that you'd run a pre-experiment power analysis and check for novelty effects by analyzing the treatment effect over time. Also, mention that you'd segment results by group size and type to ensure the feature doesn't harm smaller or specialized groups.
Restate the product goal (e.g., increase engagement in Groups) and articulate a clear hypothesis: sticker replies will lower the barrier to respond, leading to more replies and active members.
Select primary success metrics (e.g., reply rate, number of replies per post) and guardrail metrics (e.g., spam reports, hide/block rate, time spent per reply). Include secondary metrics like DAU and retention.
Propose a randomized controlled trial at the user or group level, with a control group (no sticker replies) and treatment group (with feature). Specify randomization unit, sample size, duration, and power analysis.
Plan to analyze results using appropriate statistical tests, check for novelty effects, and segment by group size/type. Evaluate trade-offs between success and guardrail metrics.
Based on results, recommend ship, iterate, or kill. Discuss potential follow-up experiments and long-term monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They wanted a real decision framework, not just 'look at p-values and ship it.' I talked through segmenting by group type and size because stickers probably behave differently in a close-knit family group versus a large public community.
Start by clarifying the experiment's goal, primary metric, and guardrail metrics. Then walk through a structured analysis: check validity, measure impact, assess trade-offs, and decide based on statistical and practical significance. Emphasize that the decision should align with product strategy and long-term goals, not just short-term metrics.
Pro tip: Always consider the novelty effect and long-term impact—Meta often runs holdout experiments to measure sustained effects. Also, be prepared to discuss how you'd handle multiple comparisons and peeking, as these are common pitfalls in A/B testing.
Confirm the hypothesis, primary metric, guardrail metrics, and expected effect size. Ensure you understand the randomization unit and target population.
Check for sample ratio mismatch (SRM), data quality issues, and whether the experiment ran for the planned duration. Ensure no peeking or multiple testing biases.
Compute the treatment effect, confidence intervals, and p-values for the primary metric. Examine secondary metrics and guardrails to understand trade-offs.
Evaluate whether the effect size is meaningful for the business. Look for heterogeneous treatment effects across key segments (e.g., new vs. existing users).
Weigh statistical significance, practical significance, and strategic alignment. Decide to launch, iterate, or abandon, and propose follow-up experiments if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.