Interpret no significant difference from an experiment

Last updated: August 26, 2025

Quick Overview

An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?

Netflix
Analytics & Experimentation
Data Scientist
Netflix
August 26, 2025
Data Scientist
Phone Screen
Analytics & Experimentation
Medium

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4,315 solved


An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?

Netflix 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
  • 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
Metric definition and success criteria
Long-term vs short-term metrics
Guardrail metrics
Funnel analysis and cohort analysis
Segmentation and heterogeneous effects
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 would you do if a stakeholder wants to end the experiment early because initial results look good?
  • What if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
  • How would you handle interference between treatment and control?
  • What if you discover a bug in the logging during the experiment?
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