Calculate probability for user retention

Last updated: September 3, 2025

Quick Overview

Given the following scenario about user conversion, calculate the the sample size needed.

Palo Alto Networks
Analytics & Experimentation
Data Scientist
Palo Alto Networks
September 3, 2025
Data Scientist
Technical Screen
Analytics & Experimentation
Hard

326

15

4,202 solved


Given the following scenario about user conversion, calculate the the sample size needed.

Analytics questions at Palo Alto Networks evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Technical Screen question tests your end-to-end analytical thinking.

What the Interviewer Expects
  • Design complex experimentation strategies for tricky scenarios
  • Handle multi-armed bandits, switchback experiments, and quasi-experiments
  • Address long-term effects vs short-term metrics
  • Propose causal inference methods when randomization is not possible
  • Build a measurement framework that connects metrics to business value
  • Discuss organizational experimentation culture and maturity
Key Topics to Cover
Segmentation and heterogeneous 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
  • How would you handle seasonality in your experiment?
  • How would you handle an experiment where the control and treatment groups are different sizes?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
  • What if you discover a bug in the logging during the experiment?
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