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.

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Aug 4, 2025, 10:55 AM
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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?
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