Design an A/B test for a new checkout flow
Last updated: September 6, 2025
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Bloomberg
September 6, 20251
13
1,277 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
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
- How would you handle interference between treatment and control?
- 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?
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Browse Analytics QuestionsSample Answer
Problem Setup
To evaluate the impact of new pricing tiers on the checkout flow, we need to frame the analytical question: **How do the new pricing tiers affect conversion rates and average order value (AOV) compare...
Methodology
We will use a randomized controlled trial (RCT) design for the A/B test. The sample size calculation will be based on the expected effect size, baseline conversion rate, and desired statistical power ...
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