Calculate probability for A/B test results
Last updated: June 6, 2026
Quick Overview
Given the following scenario about user conversion, calculate the the p-value.
Elastic
June 6, 202610
5
3,980 solved
Given the following scenario about user conversion, calculate the the p-value.
This analytics question from Elastic's Phone 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
- How would you handle seasonality in your experiment?
- 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?
- How would you handle interference between treatment and control?
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Browse Analytics QuestionsSample Answer
Problem Setup
To calculate the p-value for the A/B test results, we need to frame the analytical question: "Is there a statistically significant difference in user conversion rates between Group A (control) and Gro...
Methodology
We will use a two-proportion z-test to compare the conversion rates of the two groups. The null hypothesis (H0) states that there is no difference in conversion rates (p_A = p_B), while the alternativ...
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