Interpret a statistically significant lift of 2% from an experiment
Last updated: November 10, 2025
Quick Overview
An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
Figma
November 10, 202528
6
4,075 solved
An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
Figma asks this during the Phone Screen 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
- Define clear success metrics aligned with business goals
- Propose a basic experimental design with control and treatment groups
- Interpret results correctly and draw reasonable conclusions
- Identify obvious confounding variables
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 the experiment shows a positive short-term effect but you suspect a negative long-term impact?
- 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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Problem Setup
In this scenario, Figma has conducted an A/B experiment that resulted in a statistically significant lift of 2% in a specific metric (e.g., user engagement or conversion rate). The analytical question...
Methodology
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