Interpret mixed results across segments from an experiment
Last updated: March 16, 2026
Quick Overview
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
Lyft
March 16, 202634
4
4,997 solved
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
Lyft asks this during the Onsite to assess your experimentation skills. They want to see how you define success metrics, design controlled experiments, and interpret results with appropriate statistical rigor.
What the Interviewer Expects
- Design a rigorous experiment with proper randomization and sample size calculation
- Define primary and guardrail metrics with clear rationale
- Address novelty effects, network effects, and interference
- Segment results appropriately and identify heterogeneous treatment effects
- Propose follow-up analyses when results are ambiguous
Key Topics to Cover
How to Approach This
- Define success metrics carefully. A good metric is measurable, actionable, and aligned with business goals.
- Run experiments long enough to account for novelty effects and weekly seasonality.
- Use funnel analysis to identify where users drop off for maximum optimization impact.
- Segment results by key dimensions (platform, country, user cohort) to catch hidden patterns.
- Consider network effects and interference between treatment and control groups.
Possible Follow-up Questions
- What if you discover a bug in the logging during the experiment?
- How would you handle an experiment where the control and treatment groups are different sizes?
- What would you do if a stakeholder wants to end the experiment early because initial results look good?
- How would you handle interference between treatment and control?
Sharpen Your Skills on Codemia
Practice similar problems with our interactive workspace, get AI feedback, and track your progress.
Browse Analytics QuestionsSample Answer
Problem Setup
The analytical question at hand is to interpret the results of an A/B test conducted by Lyft, which indicates a 3% lift in a key metric (e.g., ride bookings) with a p-value of 0.08. To analyze this, w...
Methodology
For this analysis, I will utilize a two-sample proportion test to evaluate the statistical significance of the observed 3% lift. Given that the p-value is 0.08, which is above the common alpha level o...
Submit Your Answer
