Explain p-value with an example
Last updated: October 9, 2025
Quick Overview
Explain p-value in simple terms and provide a concrete example.
MongoDB
Statistics & Math
Data Scientist
MongoDB
October 9, 2025Data Scientist
Onsite
Statistics & Math
Easy
43
5
3,196 solved
Explain p-value in simple terms and provide a concrete example.
MongoDB values data-driven decision making. This Onsite 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
- State the correct formula or theorem with clear definitions
- Apply the concept to the given scenario step by step
- Interpret the result in plain language
- Identify assumptions and when they might be violated
Key Topics to Cover
Hypothesis testing (H0, H1, p-values)
Conditional probability and Bayes theorem
Power analysis and sample size calculation
Multiple testing correction (Bonferroni, FDR)
Causal inference basics
Central Limit Theorem
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 if the sample size is very small?
- How would you explain this result to a non-technical audience?
- What assumptions does this test make, and how would you validate them?
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Browse Statistics QuestionsSample Answer
Problem Formulation
In hypothesis testing, we start with two competing hypotheses:
- Null Hypothesis (H0): This represents a statement of no effect or no difference. For example, let's say we want to test whether a ...
Solution Approach
To compute the p-value, we typically follow these steps:
- Conduct an experiment: Gather data by running an A/B test where one group uses the new feature (treatment group) and another group does ...
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