Instacart
Hard
Data ScientistCompute duration and stopping rules correctly
Analytics & Experimentation
6
0
October 13, 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
Shopify
Hard
Machine Learning EngineerCollect labels without existing data
Analytics & Experimentation
5
0
September 6, 2025
Google
Hard
Data ScientistBoost Google Workspace Chat Usage with Strategic A/B Testing
Analytics & Experimentation
16
0
August 4, 2025
Meta
Medium
Data Scientist[Analytical Reasoning] Comparing Two Newsfeed Ad Insertion Methods
Analytics & Experimentation
16
0
April 7, 2025
Airbnb
Medium
Data ScientistDesign a network-aware Wi‑Fi badge experiment
Analytics & Experimentation
8
0
October 13, 2025
Amazon
Medium
Data ScientistExplore Dataset to Assess Quality and Choose Visualizations
Analytics & Experimentation
26
0
July 12, 2025
Airbnb
Medium
Data ScientistAnalyze A/B Test Results to Inform Stakeholder Decisions
Analytics & Experimentation
17
0
July 12, 2025
Meta
Medium
Data ScientistShould WhatsApp launch group calls?
Analytics & Experimentation
8
0
March 24, 2026
PayPal
Easy
Data ScientistDesign and evaluate a fraud detection strategy
Analytics & Experimentation
4
0
January 17, 2026
Meta
Hard
Data ScientistMeasure impact of bot mitigation via experiment
Analytics & Experimentation
6
0
October 13, 2025
Uber
Hard
Data ScientistChoose between A/B and switchback for spillovers
Analytics & Experimentation
7
0
October 13, 2025
Uber
Hard
Data ScientistDesign and power an incentive experiment
Analytics & Experimentation
9
0
October 13, 2025
Uber
Hard
Data ScientistDesign and Evaluate an Experiment on Surge
Analytics & Experimentation
7
0
October 13, 2025
Gusto
Medium
Data ScientistInvestigate Causes of Increased Payroll Processing Time
Analytics & Experimentation
8
0
August 4, 2025
DoorDash
Medium
Data ScientistIdentify Major Components of DoorDash's Operational Costs
Analytics & Experimentation
20
0
July 12, 2025
DoorDash
Easy
Data ScientistHow would you test a bike delivery option?
Analytics & Experimentation
5
0
February 12, 2026
Uber
Medium
Data ScientistEvaluate Marketplace Changes
Analytics & Experimentation
4
0
February 27, 2026
LinkedIn
Easy
Data ScientistResolve Simpson’s paradox in email A/B test
Analytics & Experimentation
6
0
February 16, 2026
Meta
Easy
Data ScientistDefine engagement metrics and analyze comment distribution
Analytics & Experimentation
7
0
December 6, 2025
Airbnb
Hard
Data ScientistDesign robust primary and guardrail metrics
Analytics & Experimentation
4
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.