Samsung
Medium
Data ScientistPlan parking demand for a retail store
Analytics & Experimentation
0
0
March 19, 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
Filter and sort
PayPal
Hard
Data ScientistEvaluate a New Homepage Feature
Analytics & Experimentation
0
0
October 29, 2025
Meta
Hard
Data ScientistDesign and justify unread-account pinning experiment
Analytics & Experimentation
0
0
October 13, 2025
PayPal
Easy
Data ScientistEvaluate smart cart idea and design experiment
Analytics & Experimentation
0
0
October 10, 2025
Meta
Hard
Data ScientistTest if social users are more engaged
Analytics & Experimentation
0
0
August 17, 2025
Meta
Medium
Data ScientistDefine and Measure Effective Read on Newsfeed
Analytics & Experimentation
0
0
August 4, 2025
TikTok
Medium
Data ScientistDetermine Metrics for Evaluating Homepage Recommendation Carousel
Analytics & Experimentation
0
0
August 4, 2025
Meta
Medium
Data ScientistEvaluate a new-listing notification feature
Analytics & Experimentation
0
0
February 15, 2026
Tradedesk
Hard
Data ScientistEvaluate and safely deploy a CVR model online
Analytics & Experimentation
0
0
December 10, 2025
Meta
Hard
Data ScientistValidate in-post restaurant recommendations via experiment
Analytics & Experimentation
0
0
October 13, 2025
Capital One
Medium
Data ScientistDesign metrics and an A/B test for an app
Analytics & Experimentation
0
0
October 13, 2025
Capital One
Hard
Data ScientistDesign a profit growth strategy
Analytics & Experimentation
0
0
October 13, 2025
Meta
Hard
Data ScientistDesign experiment for Group Calls with interference
Analytics & Experimentation
0
0
October 13, 2025
Meta
Hard
Data ScientistDesign and analyze notification pinning experiment
Analytics & Experimentation
0
0
October 13, 2025
TikTok
Hard
Data ScientistDesign an experiment for exploratory recommendations
Analytics & Experimentation
0
0
July 13, 2025
LinkedIn
Hard
Data ScientistAnalyze Profile Traffic Drop
Analytics & Experimentation
0
0
January 19, 2026
Glean
Easy
Data ScientistHow to measure product success?
Analytics & Experimentation
0
0
January 11, 2026
Meta
Hard
Data ScientistHow to evaluate Shop ad upranking
Analytics & Experimentation
0
0
October 26, 2025
Other
Medium
Data ScientistDesign and power an A/B on question mix
Analytics & Experimentation
0
0
October 13, 2025
Capital One
Hard
Data ScientistDesign profit evaluation for loyalty program
Analytics & Experimentation
0
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.