Interpret a statistically significant lift of 2% from an experiment

Last updated: January 15, 2026

Quick Overview

An experiment shows conflicting results. What conclusions can you draw? What are the caveats?

Zillow
Analytics & Experimentation
Data Scientist
Zillow
January 15, 2026
Data Scientist
Phone Screen
Analytics & Experimentation
Easy

73

5

219 solved


An experiment shows conflicting results. What conclusions can you draw? What are the caveats?

Zillow 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
Novelty and primacy effects
Simpson's paradox and ecological fallacy
A/B testing methodology
Segmentation and heterogeneous effects
Long-term vs short-term metrics
How to Approach This
  1. Define success metrics carefully. A good metric is measurable, actionable, and aligned with business goals.
  2. Run experiments long enough to account for novelty effects and weekly seasonality.
  3. Use funnel analysis to identify where users drop off for maximum optimization impact.
  4. Segment results by key dimensions (platform, country, user cohort) to catch hidden patterns.
  5. Consider network effects and interference between treatment and control groups.
Possible Follow-up Questions
  • What if you discover a bug in the logging during the experiment?
  • 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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