Interpret a statistically significant lift of 2% from an experiment
Last updated: August 19, 2025
Quick Overview
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
SpaceX
August 19, 2025300
9
3,318 solved
An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?
SpaceX values data-driven decision making. This Technical Screen question assesses whether you can design experiments, interpret results correctly, and avoid common statistical pitfalls like p-hacking or Simpson's paradox.
What the Interviewer Expects
- Set up the problem formally with proper notation
- Apply the correct statistical test with clear justification
- Interpret results with appropriate caveats and confidence levels
- Discuss practical significance vs statistical significance
- Identify potential confounders and how to address them
Key Topics to Cover
How to Approach This
- Define your hypotheses (H0 and H1) clearly before performing any test.
- Calculate required sample size BEFORE running an experiment, using power analysis.
- Remember the Central Limit Theorem: sample means become approximately normal with large n.
- Watch for Simpson's paradox. Always segment data by key dimensions.
- Distinguish between statistical significance and practical significance.
Possible Follow-up Questions
- What alternative statistical method could you use here?
- How would you handle multiple comparisons?
- What assumptions does this test make, and how would you validate them?
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Problem Formulation
In this scenario, we are testing whether a new approach or technology has a significant effect on performance metrics at SpaceX, measured as a percentage lift. Let:
- : There is no eff...
Solution Approach
To interpret the results, we need to analyze the p-value in relation to the chosen significance level (alpha = 0.05). The steps are as follows:
- Compare p-value to alpha: Since 0.08 > 0.05, we fail ...
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