Calculate probability for A/B test results
Last updated: May 13, 2026
Quick Overview
Given the following scenario about revenue per session, calculate the the sample size needed.
DoorDash
May 13, 20263
6
385 solved
Given the following scenario about revenue per session, calculate the the sample size needed.
Analytics questions at DoorDash 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
How to Approach This
- Define success metrics carefully. A good metric is measurable, actionable, and aligned with business goals.
- Run experiments long enough to account for novelty effects and weekly seasonality.
- Use funnel analysis to identify where users drop off for maximum optimization impact.
- Segment results by key dimensions (platform, country, user cohort) to catch hidden patterns.
- Consider network effects and interference between treatment and control groups.
Possible Follow-up Questions
- How would you handle interference between treatment and control?
- 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?
- What if you discover a bug in the logging during the experiment?
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Browse Analytics QuestionsSample Answer
Problem Setup
The analytical question is: "What is the sample size required to detect a significant difference in revenue per session between the control and treatment groups in an A/B test for DoorDash?" To answer...
Methodology
To calculate the sample size needed for the A/B test, we can use the formula for sample size in comparing two means, assuming normal distribution:
[ n = \left( \frac{(Z_{\alpha/2} + Z_{\beta})^2 ...
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