DoorDash
Medium
Data ScientistHow to test bike delivery?
Analytics & Experimentation
17
0
March 1, 2026
A/B Testing Interview Questions
A/B testing questions are central to data science and product analytics interviews at companies like Meta, Google, Netflix, and Airbnb.
Expect questions on experiment design, randomization units, sample size calculation, multiple comparisons, and metric selection.
Interviewers evaluate your statistical rigor, practical judgment, and ability to communicate experiment results.
837Questions
79Companies
Easy 99 • Medium 59 • Hard 12Difficulty mix
Common A/B testing interview patterns
- Designing an experiment for a product change
- Calculating sample size and experiment duration
- Choosing between one-sided and two-sided tests
- Handling multiple comparisons and peeking
- Interpreting results with novelty or primacy effects
- Network effects and interference between test groups
A/B testing interview questions
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DoorDash
Hard
Data ScientistDiagnose and experiment to reduce late deliveries
Analytics & Experimentation
17
0
October 13, 2025
Chime
Medium
Data ScientistDetermine Key Metrics for Spend-Tracker Launch Decision
Analytics & Experimentation
71
0
August 4, 2025
LinkedIn
Medium
Data ScientistDiagnose Job Application Decline: Funnel Analysis and Segmentation
Analytics & Experimentation
69
0
July 12, 2025
Meta
Hard
Data ScientistDetermine Demand for WhatsApp Group Video-Calls
Analytics & Experimentation
68
0
July 12, 2025
Upstart
Hard
Data ScientistDesign Experiment to Measure Airport Surge-Pricing Impact
Analytics & Experimentation
58
0
August 4, 2025
Snapchat
Medium
Data ScientistDesign A/B Tests for Banner Ad and Group-Story Feature
Analytics & Experimentation
55
0
July 12, 2025
LinkedIn
Hard
Data ScientistDesign Experiments for Email Campaign & Messaging Update
Analytics & Experimentation
68
0
July 12, 2025
Meta
Hard
Data ScientistAnalyze Data to Boost Group Post Comment Rates
Analytics & Experimentation
68
0
July 12, 2025
Upstart
Easy
Data ScientistEstimate impact without experiments and pick variant
Analytics & Experimentation
13
0
February 19, 2026
Chime
Hard
Data ScientistDesign and Analyze A/B Test for Recommendation Widget
Analytics & Experimentation
68
0
August 4, 2025
Meta
Medium
Data ScientistMeasure Harmful Content Impact with Key Metrics
Analytics & Experimentation
60
0
August 4, 2025
Boston Consulting Group
Medium
Data ScientistIdentify Causes and Solutions for Fashion Profit Decline
Analytics & Experimentation
60
0
August 4, 2025
Meta
Hard
Data ScientistAnalyze Algorithm's Impact on Diverse Demographics and Validate Causes
Analytics & Experimentation
61
0
August 4, 2025
Capital One
Medium
Data ScientistEvaluate Factors Before Renewing TV-Series Contracts
Analytics & Experimentation
62
0
July 12, 2025
Uber
Medium
Data ScientistEvaluate ETA Impact on Conversion
Analytics & Experimentation
12
0
February 14, 2026
Boston Consulting Group
Medium
Data ScientistEvaluate Campaign Lift with Predictive Analytics and Validation Strategy
Analytics & Experimentation
56
0
August 4, 2025
OpenAI
Hard
Data ScientistDesign a free-month experiment
Analytics & Experimentation
15
0
February 3, 2026
Capital One
Medium
Data ScientistIdentify Key Profit Factors for $54 Premium Plan
Analytics & Experimentation
63
0
July 12, 2025
Uber
Hard
Data ScientistExplain and validate A/B test assumptions
Analytics & Experimentation
10
0
October 13, 2025
Common mistakes in A/B testing interviews
- Not specifying the randomization unit (user vs session vs page)
- Peeking at results before reaching the required sample size
- Ignoring practical significance when statistical significance is achieved
- Not considering guardrail metrics
- Failing to account for novelty effects in short experiments
How A/B testing questions are evaluated
Structure your experiment design: hypothesis, metrics, unit, sample size, duration.
Discuss what could go wrong and how you would detect it.
Show ability to make a recommendation even when results are ambiguous.
Related analytics concepts
A/B Testing Interview FAQs
How do you determine the sample size for an A/B test?
Use a power analysis with inputs: baseline metric, minimum detectable effect (MDE), significance level (alpha, usually 0.05), and power (usually 0.80). Larger effects need fewer samples. For small MDE on rare events, you may need millions of users.
What is the difference between statistical and practical significance?
Statistical significance means the observed difference is unlikely due to chance (p-value < alpha). Practical significance means the effect is large enough to matter for the business. A statistically significant 0.01% lift may not be worth the engineering cost to ship.