Identify P-Value Limitations and Complementary Approaches
Quick Overview
Identify P-Value Limitations and Complementary Approaches evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Identify P-Value Limitations and Complementary Approaches
Company: Amazon
Role: Data Scientist
Category: Statistics & Math
Difficulty: medium
Interview Round: Onsite
##### Scenario
During an A/B test for a new product feature, stakeholders rely on p-values to decide shipment.
##### Question
Explain at least three key limitations of p-values in hypothesis testing and suggest complementary measures or approaches that mitigate these issues.
##### Hints
Think about effect size, sample size dependence, multiple testing, practical significance, prior beliefs, and p-hacking.
Quick Answer: Identify P-Value Limitations and Complementary Approaches evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Identify P-Value Limitations and Complementary Approaches
Amazon
Aug 4, 2025, 10:55 AM
mediumData ScientistOnsiteStatistics & Math
19
0
Identify P-Value Limitations and Complementary Approaches
A/B Testing: Limits of P-values and Better Decision Practices
Scenario
Your team is running an A/B test for a new product feature and stakeholders rely on p-values to decide whether to ship.
Task
Explain at least three key limitations of p-values in hypothesis testing and suggest complementary measures or approaches that mitigate these issues. Use A/B testing context and brief examples where helpful.
Guidance
Consider topics such as: effect size, dependence on sample size, multiple testing, practical significance, prior beliefs, and p-hacking/data peeking.
Clarifying Questions to Ask Guidance
Clarify the random variables, distributional assumptions, independence assumptions, and desired output.
Show enough derivation for the interviewer to follow the reasoning.
Explain how you would validate the result with simulation or sensitivity checks.
What a Strong Answer Covers Guidance
A correct setup with definitions, formulas, and boundary conditions.
A step-by-step derivation or estimation plan.
Interpretation of the result, including uncertainty and practical limitations.
Checks for assumptions, edge cases, and numerical stability.
Follow-up Questions Guidance
How would the result change if the assumptions were relaxed?
Can you verify the answer with a simulation?
What is the most likely source of estimation error?