Calculate expected value for user retention

Last updated: August 30, 2025

Quick Overview

Given the following scenario about revenue per session, calculate the the p-value.

Databricks
Analytics & Experimentation
Product Manager
Databricks
August 30, 2025
Product Manager
Phone Screen
Analytics & Experimentation
Medium

3

5

2,489 solved


Given the following scenario about revenue per session, calculate the the p-value.

Databricks 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
Segmentation and heterogeneous effects
Novelty and primacy effects
Guardrail metrics
Sample size and power calculation
Simpson's paradox and ecological fallacy
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 the experiment shows a positive short-term effect but you suspect a negative long-term impact?
  • How would you handle an experiment where the control and treatment groups are different sizes?
  • 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?
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