Interpret no significant difference from an experiment

Last updated: October 21, 2025

Quick Overview

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

Uber
Analytics & Experimentation
Data Scientist
Uber
October 21, 2025
Data Scientist
Take-home Project
Analytics & Experimentation
Easy

29

0

3,198 solved


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

Analytics questions at Uber evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Take-home Project 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
Long-term vs short-term metrics
Metric definition and success criteria
Funnel analysis and cohort analysis
Guardrail metrics
Segmentation and heterogeneous effects
A/B testing methodology
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 if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
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