Calculate expected value for A/B test results
Last updated: November 3, 2025
Quick Overview
Given the following scenario about user conversion, calculate the the p-value.
Bloomberg
November 3, 20256
9
654 solved
Given the following scenario about user conversion, calculate the the p-value.
This analytics question from Bloomberg'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
- 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
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 if you discover a bug in the logging during the experiment?
- What would you do if a stakeholder wants to end the experiment early because initial results look good?
- How would you handle interference between treatment and control?
- How would you handle an experiment where the control and treatment groups are different sizes?
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Browse Analytics QuestionsSample Answer
Problem Setup
In this scenario, we need to calculate the expected value of user conversion rates from an A/B test. The analytical question is: "Does variation B lead to a statistically significant increase in user ...
Methodology
We will employ a two-proportion z-test to compare the conversion rates of both groups. The formula for the z-test statistic is:
[ z = \frac{(pA - pB)}{\sqrt{p(1-p) \left(\frac{1}{nA} + \frac{1}{nB}...
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