Calculate conditional probability for A/B test results

Last updated: March 29, 2026

Quick Overview

Given the following scenario about user conversion, calculate the the p-value.

Palo Alto Networks
Analytics & Experimentation
Data Scientist
Palo Alto Networks
March 29, 2026
Data Scientist
Technical Screen
Analytics & Experimentation
Medium

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7

63 solved


Given the following scenario about user conversion, calculate the the p-value.

Palo Alto Networks asks this during the Technical 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
Sample size and power calculation
Metric definition and success criteria
Novelty and primacy effects
A/B testing methodology
Segmentation and heterogeneous effects
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
  • How would you handle interference between treatment and control?
  • 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 the experiment shows a positive short-term effect but you suspect a negative long-term impact?
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