Interpret a statistically significant lift of 2% from an experiment
Last updated: January 31, 2026
Quick Overview
An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
Snapchat
January 31, 202645
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325 solved
An experiment shows conflicting results. What conclusions can you draw? What are the caveats?
Analytics questions at Snapchat evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Onsite question tests your end-to-end analytical thinking.
What the Interviewer Expects
- Design complex experimentation strategies for tricky scenarios
- Handle multi-armed bandits, switchback experiments, and quasi-experiments
- Address long-term effects vs short-term metrics
- Propose causal inference methods when randomization is not possible
- Build a measurement framework that connects metrics to business value
- Discuss organizational experimentation culture and maturity
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 the experiment shows a positive short-term effect but you suspect a negative long-term impact?
- What if you discover a bug in the logging during the experiment?
- 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
The analytical question here is: "What does a statistically significant lift of 2% in user engagement from our recent experiment indicate, and how should we interpret these results?" To answer this, w...
Methodology
To analyze the lift, we can use a two-proportion z-test to compare the control and treatment groups' engagement rates. The formula for the z-test is:
[ z = \frac{(p_1 - p_2)}{\sqrt{p(1-p)(\frac{1}{n...
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