Interpret mixed results across segments from an experiment
Last updated: September 27, 2025
Quick Overview
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
Scale AI
September 27, 2025154
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An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
This analytics question from Scale AI'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
- 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 seasonality in your experiment?
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Browse Analytics QuestionsSample Answer
Problem Setup
The analytical question at hand is to interpret a 3% lift observed in an experiment with a p-value of 0.08. To effectively analyze this, we need data on:
- Sample size: The total number of partici...
Methodology
Given that we have a p-value of 0.08, we are on the edge of conventional significance (typically p < 0.05). To draw conclusions:
- Cohort Analysis: Evaluate the treatment effect across different ...
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