Interpret mixed results across segments from an experiment
Last updated: October 7, 2025
Quick Overview
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
HRT
October 7, 202525
6
2,019 solved
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
Analytics questions at HRT evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Onsite 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 an experiment where the control and treatment groups are different sizes?
- How would you handle seasonality in your experiment?
- What if you discover a bug in the logging during the experiment?
- How would you handle interference between treatment and control?
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Browse Analytics QuestionsSample Answer
Problem Setup
The analytical question is: "What does a 3% lift in our experiment with a p-value of 0.08 indicate about the effectiveness of our intervention across different segments?" To analyze this, we need data...
Methodology
For this analysis, I would employ a two-sample proportion test to compare the conversion rates between the control and treatment groups. The formula for the test statistic is:
[ z = \frac{(p_1 - p_2...
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