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Design an A/B test for WhatsApp call reliability

Last updated: Mar 29, 2026

Quick Overview

This question evaluates experimental design and statistical reasoning for real-time calling services, focusing on two-sided treatment exposure, interference, metric definition, clustering/repeat-measures handling, and power/sample-size computation.

  • hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Design an A/B test for WhatsApp call reliability

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

You join WhatsApp’s Calling org. PM proposes enabling an adaptive codec for unstable networks to reduce call drops. Design an A/B test that: (a) chooses the randomization unit (caller-level, callee-level, dyad-level, or geo cluster) and justifies it under two-sided exposure/interference; (b) defines primary and guardrail metrics precisely (e.g., call-drop rate = dropped calls / initiated calls excluding employee/test accounts; connection latency p95; complaint rate), and specifies the exposure logic (feature ‘on’ only when both sides are treated vs when caller is treated); (c) outlines a ramp plan and spillover checks; (d) handles non-independence (repeat callers) and seasonality; (e) provides a power/SST back-of-envelope: baseline drop rate 3.2%, target 8% relative reduction, alpha=0.05 (two-sided), power=0.8, 30-day test, average 4 calls/day/caller, caller-level ICC ρ=0.10. Compute the design effect and approximate number of unique callers per arm needed under your chosen unit. Finally, specify the estimand and estimator (e.g., ITT at caller-level with cluster-robust SEs) and how you will diagnose interference (e.g., cross-arm caller–callee edges).

Quick Answer: This question evaluates experimental design and statistical reasoning for real-time calling services, focusing on two-sided treatment exposure, interference, metric definition, clustering/repeat-measures handling, and power/sample-size computation.

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Meta
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Analytics & Experimentation
1
0

A/B Test Design: Adaptive Codec for Unstable Networks (WhatsApp Calling)

Context

You join the Calling organization. A PM proposes enabling an adaptive codec that activates on unstable networks to reduce call drops. The codec is two-sided: it can only run when both participants support it. Your task is to design and size an A/B test that accounts for two-sided exposure and network interference.

Assume we are testing on 1:1 calls (audio and/or video). Unless stated, exclude employees/test accounts and spam/abuse traffic. Assume the baseline drop rate below refers to calls under unstable network conditions (the codec's target population).

Tasks

(a) Choose the randomization unit (caller-level, callee-level, dyad-level, or geo cluster) and justify it under two-sided exposure/interference.

(b) Define primary and guardrail metrics precisely, and specify exposure logic (feature on only when both sides are treated vs when caller alone is treated).

(c) Outline a ramp plan and spillover checks.

(d) Handle non-independence (repeat callers) and seasonality.

(e) Provide a power/SST back-of-the-envelope. Assume:

  • Baseline drop rate p0 = 3.2%
  • Target = 8% relative reduction
  • Alpha = 0.05 (two-sided)
  • Power = 0.80
  • Test length = 30 days
  • Average 4 calls/day per caller
  • Caller-level ICC ρ = 0.10 Compute the design effect and the approximate number of unique callers per arm needed under your chosen randomization unit.

Finally, specify the estimand and estimator (e.g., ITT at caller-level with cluster-robust SEs), and how you will diagnose interference (e.g., cross-arm caller–callee edges).

Solution

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