Interpret a statistically significant lift of 2% from an experiment
Last updated: April 10, 2026
Quick Overview
An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
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An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
Reddit asks this during the Take-home Project 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
- How would you handle seasonality in your experiment?
- What would you do if a stakeholder wants to end the experiment early because initial results look good?
- How would you handle an experiment where the control and treatment groups are different sizes?
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