Calculate variance for A/B test results

Last updated: August 21, 2025

Quick Overview

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

HRT
Analytics & Experimentation
Data Scientist
HRT
August 21, 2025
Data Scientist
Technical Screen
Analytics & Experimentation
Easy

9

5

3,896 solved


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

This analytics question from HRT's Technical Screen tests your ability to think critically about data. The interviewer expects you to consider confounding variables, selection bias, and the difference between correlation and causation.

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
Guardrail metrics
Network effects and interference
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 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?
  • How would you handle interference between treatment and control?
  • What if you discover a bug in the logging during the experiment?
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