Design an A/B test for a recommendation algorithm
Last updated: January 8, 2026
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Anduril
January 8, 20266
6
599 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Analytics questions at Anduril 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 seasonality in your 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?
- 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
To evaluate the impact of new pricing tiers on the effectiveness of our recommendation algorithm, we need to define a clear analytical question: **How do the new pricing tiers influence user engagemen...
Methodology
For this A/B test, we will implement a randomized controlled trial design. Experimental Design:
- Control Group: Users exposed to the existing pricing tiers.
- Treatment Group: Users e...
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