Interpret a statistically significant lift of 2% from an experiment
Last updated: April 30, 2026
Quick Overview
An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
Brex
April 30, 202650
4
1,333 solved
An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
Analytics questions at Brex evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Take-home Project question tests your end-to-end analytical thinking.
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
- How would you handle interference between treatment and control?
- How would you handle an experiment where the control and treatment groups are different sizes?
- What if you discover a bug in the logging during the experiment?
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Browse Analytics QuestionsSample Answer
Problem Setup
The analytical question at hand is to interpret a statistically significant lift of 2% from an experiment conducted by Brex. To effectively analyze this result, we need to define clear success metrics...
Methodology
To analyze the 2% lift, we should perform a hypothesis test (specifically a two-sample t-test) to compare the means of the treatment and control groups. The null hypothesis (H0) would state that there...
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