Master A/B Testing: Key Concepts and Methodologies Explained

Q: Master A/B Testing: Key Concepts and Methodologies Explained

This question evaluates a data scientist's practical mastery of A/B testing and causal inference, covering statistical concepts (p-values, Type I/II errors and power), experimental design (sample size, segmentation, variance, metrics) and nonrandomized causal methods.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

A/B Testing and Causal Inference: Core Concepts and Practice

Scenario

You are a data scientist interviewing for a role working on an online product. You are asked to demonstrate practical A/B testing and causal inference knowledge.

Task

Provide concise, accurate explanations and practical guidance for the following:

P-value: What it represents and common misinterpretations.
Errors: Define Type I and Type II errors; relate to power.
Experimentation Workflow: Outline an end-to-end process from hypothesis to decision, including sample size, segmentation, and variance considerations.
Simpson's Paradox: Define, give a practical example, and explain how to detect and handle it.
Metrics: Propose primary and secondary/guardrail metrics for an online product experiment.
Causal Inference Without Randomization: Name two useful methods and when you would apply each.

Hints to address: hypothesis clarity, sample-size (power) calculation, segmentation, lift vs variance, DAGs or matching, and practical examples.

Master A/B Testing: Key Concepts and Methodologies Explained

A/B Testing and Causal Inference: Core Concepts and Practice

Scenario

Task

Solution

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