314,552 interview questions from 6,000+ companies.
Explain what a p-value means in hypothesis testing and how it relates to statistical significance.
Explain what statistical significance means and why it matters when interpreting experimental or analytical results.
Explain how to design and evaluate an A/B test for a product feature, including metrics, MDE, sample size, and guardrails.
Tests your ability to design rigorous experiments aligned to testable hypotheses.
Explain how to test whether an observed experiment lift is real using hypothesis testing, p-values, and confidence intervals.
Reason about sample size, power, and minimum detectable effect before launching an experiment.
Explain how to evaluate whether an A/B test result is statistically significant and how to interpret the result.
Explain how you have designed and implemented A/B tests, including hypothesis setup, analysis, and decision making.
Explain statistical significance in experiments and how p-values and confidence intervals guide interpretation.
Explain how to choose an appropriate significance test based on metric type, study design, and the null hypothesis.
Explain why an observed marketing relationship can be correlated without being causal, and how you would validate a true causal effect.
Explain why correlation measures association, while causation requires evidence that changing one variable changes the other.
Explain why two metrics moving together does not prove that one causes the other, and how to assess causality more carefully.
Explain what a p-value means, how it relates to statistical significance, and how to describe it clearly to non-technical stakeholders.
Explain what statistical significance means, how p-values and confidence intervals support decisions, and why significance alone is not enough.
Explain why a statistically significant experiment result may still be too small to matter for product or business decisions.
Differentiate between Type I and Type II errors in hypothesis testing with a practical example.
Reason about power analysis when planning an experiment and choosing sample size.
Explain how to test whether an observed 5% conversion rate drop is statistically significant in an experiment or before-after comparison.
Choose sample size and runtime by combining baseline rate, MDE, alpha, power, and expected traffic.
7,826 total questions
