Apple·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Apple DS interview with a meaty A/B testing design question. One question, but it had a lot of moving parts and they clearly wanted you to think through the whole thing, not just recite a formula.

Questions Asked (1)

Walk through how you would design an experiment to measure the impact of a new homepage layout on purchase rate, covering population definition, metrics, randomization strategy, sample size calculation, test duration, bias control, and analysis plan.

A/B Testing & ExperimentationProduct Analytics & Metrics

Author's notes

This one sprawled.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a clear, step-by-step experimental design, starting with defining the population and metrics, then moving through randomization, sample size, duration, bias control, and analysis. Emphasize practical considerations like guardrail metrics and novelty effects, and tie everything back to Apple's high standards for user experience and data privacy.

Pro tip: Mention that you would pre-register the analysis plan and use sequential testing or a holdout group to monitor long-term effects, showing maturity beyond basic A/B testing.

1. Define Population and Metrics

Specify the target population (e.g., all users, new vs. returning) and primary metric (purchase rate), along with secondary and guardrail metrics (e.g., revenue, engagement, latency).

2. Randomization and Sample Size

Choose a randomization unit (e.g., user-level) and calculate required sample size using power analysis, accounting for baseline rate, minimum detectable effect, and desired power.

3. Test Duration and Bias Control

Determine test duration to capture full weekly cycles and avoid novelty effects; implement safeguards like consistent assignment, bot filtering, and A/A tests to detect bias.

4. Analysis Plan

Pre-register the analysis: use appropriate statistical tests (e.g., t-test or Bayesian), check for SRM, segment results, and evaluate guardrail metrics before declaring success.

Key Points to Mention

Define success metrics: primary (purchase rate), secondary (AOV, conversion funnel), guardrails (page load time, bounce rate).
Randomization unit: user-level to avoid contamination; consider cluster randomization if network effects exist.
Sample size calculation: use power analysis (e.g., 80% power, 5% significance) and account for multiple testing corrections.
Test duration: at least one full week to capture weekly seasonality; avoid stopping early to prevent peeking bias.
Bias control: ensure random assignment, check for sample ratio mismatch (SRM), and use holdout groups for long-term effects.
Analysis: pre-register plan, use intent-to-treat, and consider heterogeneous treatment effects via segmentation.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.