Interpret a statistically significant lift of 2% from an experiment
Last updated: August 25, 2025
Quick Overview
An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
Lyft
August 25, 202576
10
2,534 solved
An experiment shows conflicting results. 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 complex experimentation strategies for tricky scenarios
- Handle multi-armed bandits, switchback experiments, and quasi-experiments
- Address long-term effects vs short-term metrics
- Propose causal inference methods when randomization is not possible
- Build a measurement framework that connects metrics to business value
- Discuss organizational experimentation culture and maturity
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
- How would you handle seasonality in your experiment?
- How would you handle interference between treatment and control?
- What would you do if a stakeholder wants to end the experiment early because initial results look good?
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Browse Analytics QuestionsSample Answer
Problem Setup
The analytical question we need to address is whether the observed 2% lift from the experiment is statistically significant and meaningful for Lyft's business objectives. Specifically, we need to ...
Methodology
To analyze the lift of 2%, we will conduct a two-sample t-test comparing the means of the control and treatment groups. The t-test will help us determine if the difference in means (the 2% lift) i...
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