Interpret mixed results across segments from an experiment

Last updated: January 9, 2026

Quick Overview

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

Notion
Analytics & Experimentation
Data Scientist
Notion
January 9, 2026
Data Scientist
Onsite
Analytics & Experimentation
Medium

7

5

85 solved


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

Notion asks this during the Onsite 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
A/B testing methodology
Long-term vs short-term metrics
Funnel analysis and cohort analysis
Network effects and interference
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?
  • 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?
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