A/B Experiment Design: Group Video Calls on Instagram
Scenario
Instagram wants to evaluate the impact of launching group video calls.
Task
Design an end-to-end experiment that accounts for strong network effects:
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State a clear hypothesis, success metrics, and guardrails.
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Choose the randomization unit (user, cluster, geography), explaining trade-offs under network interference.
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Propose a sample size and duration plan (include formulas and a small numeric example). Account for clustering via design effects.
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If clustering is infeasible, describe alternative designs to mitigate interference (e.g., two-stage/saturation, geo/switchback, encouragement designs).
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Specify which statistical tests you would use for:
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Continuous metrics (e.g., time spent, calls per user).
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Proportion metrics (e.g., % of users who made any group call).
Include differences under user-level vs. cluster/geo designs.
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Outline how you would present results to non-technical PMs vs. data-science peers.
Hints
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Discuss cluster vs. user-level assignment, geography splits, and how to measure/limit cross-arm contamination.
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Contrast t-tests and z-tests; note cluster-robust methods and randomization inference for geo/cluster designs.
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Tailor the communication: decision and business impact for PMs; assumptions, diagnostics, and methodology for DS peers.