Calculate conditional probability for coin flips
Last updated: August 26, 2025
Quick Overview
Given the following scenario about revenue per session, calculate the the sample size needed.
Microsoft
August 26, 20255
5
1,250 solved
Given the following scenario about revenue per session, calculate the the sample size needed.
This analytics question from Microsoft's Take-home Project 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
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
- What would you do if a stakeholder wants to end the experiment early because initial results look good?
- How would you handle an experiment where the control and treatment groups are different sizes?
- How would you handle seasonality in your experiment?
- What if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
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Browse Analytics QuestionsSample Answer
Problem Setup
The analytical question we need to address is: "What is the sample size required to detect a significant difference in revenue per session when comparing two groups (treatment and control) in an A/B t...
Methodology
We will use the formula for calculating the sample size in a two-sample t-test, which is appropriate for comparing the means of two independent groups. The formula is:
[ n = \left( \frac{(Z_{\alpha...
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