Experimentation and Product Validation Questions

Designing and interpreting experiments and validation strategies to test product hypotheses. Includes hypothesis formulation, experimental design, sample sizing considerations, metrics selection, interpreting results and statistical uncertainty, and avoiding common pitfalls such as peeking and multiple hypothesis testing. Also covers qualitative validation methods such as interviews and pilots, and using a mix of methods to validate product ideas before scaling.

EasyTechnical

62 practiced

Explain what an A/B test is in the context of product growth. Describe all key components you would specify before launching: hypothesis, variants, unit of randomization, primary metric, sample sizing assumptions, expected duration, and guardrails. Use a concrete example (homepage CTA color change) and explain when you would choose an A/B test versus running customer interviews or a pilot.

EasyTechnical

77 practiced

Explain the importance of choosing the correct unit of randomization (user, session, page-view, cookie, account) when running experiments. Provide a concrete example where randomizing at page-view instead of user would bias your results, and describe how you'd detect and fix that bias during analysis.

HardTechnical

53 practiced

Describe how to compute the expected value of information (VOI) for an experiment so you can decide whether the testing cost is justified by potential upside. List required inputs (uncertainty distribution over effect, business value per unit lift, cost to run experiment) and explain how VOI would influence experiment prioritization in practice.

MediumTechnical

64 practiced

You see an aggregate null effect on a pricing experiment but suspect heterogeneity across segments. Describe a principled plan for segmentation analysis: which segments to consider (business-driven), how to pre-specify comparisons to avoid cherry-picking, statistical methods to detect HTEs, and how to act on credible heterogeneous treatment effects.

EasyTechnical

63 practiced

List and define five common product metrics used for growth experiments (for example: conversion rate, activation, retention, churn, and revenue per user). For each metric briefly give one UI change that could increase it and one way that metric could be misleading without careful instrumentation.

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