Design A/B Test for Search Feature Effectiveness
Overview
This question evaluates a data scientist's skills in experimental design, A/B testing methodology, metric selection, and search relevance evaluation within the analytics & experimentation domain.
Apple
Aug 4, 2025, 10:55 AM
Data Scientist
Technical Screen
Analytics & Experimentation
28
0
A/B Testing a Search Button and Measuring Search Quality
Scenario
A product team wants to evaluate a new search button and ensure search results are high quality. As a data scientist in a technical phone screen, outline how you would design the experiment, what you would measure, and how you would assess result relevance.
Questions
- Design and run an A/B test for a new search button.
- State a clear hypothesis and define the experimental unit and randomization.
- Specify eligibility, exposure, and bucketing.
- Plan sample size and test duration; describe ramping and guardrails.
- Define primary and secondary success metrics and how you will analyze significance.
- Call out key risks and how you would mitigate them.
- For the search button, what key metrics would you track?
- Identify primary, secondary/diagnostic, and guardrail metrics.
- Include engagement, conversion, and performance/latency.
- How would you determine whether the search results are high quality?
- Describe online (behavioral) and offline (labeled) evaluation methods.
- Include relevance metrics, user feedback signals, and how you’d validate improvements.
Solution
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