DoorDash
Hard
Data ScientistIdentify Key Drivers of Delivery Decline in Los Angeles
Analytics & Experimentation
4
0
August 4, 2025
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
Filter and sort
Google
Medium
Data ScientistDesign A/B Test for Google Maps UI Change
Analytics & Experimentation
3
0
August 4, 2025
Meta
Medium
Data ScientistDesign Experiment to Test New Hashtag Recommender Algorithm
Analytics & Experimentation
12
0
July 12, 2025
Meta
Medium
Data ScientistLeverage Data Sources for Effective Push Notification Strategy
Analytics & Experimentation
7
0
July 12, 2025
Intuit
Easy
Product AnalystExtract insights from a multi-entry funnel scorecard
Analytics & Experimentation
3
0
October 21, 2025
Meta
Medium
Data ScientistReduce variance with covariate adjustment
Analytics & Experimentation
2
0
October 13, 2025
Affirm
Medium
Data ScientistDiagnose Sudden Drop in Credit-Card Approval Rate
Analytics & Experimentation
3
0
August 4, 2025
Meta
Medium
Data EngineerAnalyze private-account product metrics
Analytics & Experimentation
2
0
August 1, 2025
Meta
Medium
Data ScientistEvaluate Facebook's Restaurant Recommendations Feature Effectiveness
Analytics & Experimentation
9
0
July 12, 2025
Lyft
Medium
Data ScientistInvestigate MAU Drop and Test Coupons
Analytics & Experimentation
2
0
January 20, 2026
Instacart
Medium
Data ScientistRecommend and validate a budget allocation strategy
Analytics & Experimentation
3
0
October 13, 2025
Coinbase
Hard
Data ScientistDetect and quantify wash trading
Analytics & Experimentation
3
0
October 13, 2025
TikTok
Hard
Data ScientistTest 15s to 60s video length change
Analytics & Experimentation
3
0
October 13, 2025
Attentive
Hard
Data ScientistDiagnose March Uber ride-volume drop
Analytics & Experimentation
3
0
October 13, 2025
Meta
Medium
Product AnalystHow would you grow Meta products?
Analytics & Experimentation
3
0
March 19, 2026
LinkedIn
Hard
Data ScientistAnalyze homepage drop and feed ranking
Analytics & Experimentation
2
0
January 17, 2026
Google
Medium
Data ScientistDesign an experiment to measure latency impact
Analytics & Experimentation
2
0
November 24, 2025
Capital One
Hard
Data ScientistDefine and validate an airline profitability metric
Analytics & Experimentation
1
0
October 13, 2025
TikTok
Hard
Data ScientistDiagnose a sudden metric spike or drop
Analytics & Experimentation
2
0
October 13, 2025
Meta
Hard
Data ScientistDesign analytics and experiment for group video calls
Analytics & Experimentation
3
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.