OneMain Financial
Medium
Data ScientistCalculate Profit-Maximizing Price and Validate with Additional Data
Analytics & Experimentation
15
0
July 12, 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
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Coinbase
Medium
Data ScientistEstimate Successful Sign-ups from Super Bowl QR Code Ad
Analytics & Experimentation
19
0
July 12, 2025
DoorDash
Hard
Data ScientistDefine metrics for new market expansion success
Analytics & Experimentation
5
0
July 7, 2025
DoorDash
Hard
Data ScientistDesign an experiment for thermal bags
Analytics & Experimentation
5
0
October 13, 2025
Google
Hard
Data ScientistAnalyze time series and design validation experiment
Analytics & Experimentation
4
0
October 13, 2025
Meta
Medium
Data ScientistDetermine Metrics to Evaluate Notification Impact on Users
Analytics & Experimentation
13
0
July 12, 2025
LinkedIn
Medium
Data ScientistFrequent Traveler Case
Analytics & Experimentation
6
0
April 30, 2025
Meta
Medium
Data ScientistHow to test account ranking change
Analytics & Experimentation
2
1
March 16, 2026
Meta
Easy
Data ScientistInvestigate why an advertiser’s spend decreased
Analytics & Experimentation
3
0
February 16, 2026
Roblox
Medium
Data ScientistDetermine if players prefer local creators without experiments
Analytics & Experimentation
3
0
October 13, 2025
DoorDash
Medium
Data ScientistDesign Experiments to Measure Promotion Scheduling Impact
Analytics & Experimentation
3
0
August 4, 2025
Google
Medium
Data ScientistHow would you use propensity score matching here
Analytics & Experimentation
5
0
November 24, 2025
Robinhood
Hard
Data ScientistDiagnose sustained drop in executed trades
Analytics & Experimentation
4
0
October 13, 2025
Uber
Hard
Data ScientistInvestigate ride declines and test free trials
Analytics & Experimentation
5
1
October 13, 2025
Google
Hard
Data ScientistDiagnose and reverse an adoption-rate decline
Analytics & Experimentation
2
0
October 13, 2025
Roblox
Hard
Data ScientistDesign experiment for homepage tab replacement
Analytics & Experimentation
3
0
October 13, 2025
Microsoft
Hard
Data ScientistDesign and analyze email deliverability experiment
Analytics & Experimentation
3
1
October 13, 2025
Amazon
Hard
Machine Learning EngineerExplain Multi-Armed Bandit Principles
Analytics & Experimentation
3
0
September 6, 2025
PayPal
Medium
Data ScientistDesign and Analyze A/B Test for Cashback Program
Analytics & Experimentation
2
0
August 4, 2025
TikTok
Medium
Data ScientistDiagnose Search Issues with Relevant Metrics and Solutions
Analytics & Experimentation
15
0
July 12, 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.