I started with sample size dependence because it felt safest, basically that with enough users you can get a significant p-value on a completely trivial effect.
Acknowledge that p-values are useful for detecting statistical significance but insufficient for product decisions, then systematically explain at least three limitations (e.g., binary thinking, ignoring effect size, multiple testing, peeking, practical significance) and propose complementary methods like confidence intervals, Bayesian methods, and decision-theoretic frameworks. Emphasize that the ship decision should balance statistical evidence with business impact, risk, and cost.
Pro tip: Frame the answer around Amazon's leadership principles, especially 'Customer Obsession' and 'Deliver Results'—show that you prioritize customer impact and long-term value over statistical rituals. Mention that you'd align stakeholders on a pre-registered decision rule that incorporates both statistical and practical significance.
Briefly state that p-values measure the probability of observing data at least as extreme as the current data, assuming the null hypothesis is true. They do not measure the probability that the null is true, nor the size or importance of an effect.
Discuss at least three limitations: (1) p-values are binary and don't convey effect size or uncertainty; (2) they are sensitive to sample size and can be gamed via peeking or multiple comparisons; (3) they don't reflect practical or business significance; (4) they assume a single test and no sequential analysis.
Propose using confidence intervals to show effect size and uncertainty, Bayesian methods to directly estimate the probability that the new feature is better, and decision-theoretic approaches that weigh costs and benefits. Also suggest sequential testing or alpha-spending to handle peeking.
Explain how to incorporate business metrics (e.g., revenue, customer satisfaction) and practical significance thresholds. Recommend pre-registering the decision rule and involving stakeholders in defining what 'ship' means in terms of expected value.
Conclude that the ship decision should combine statistical evidence (e.g., Bayesian posterior probability, confidence intervals) with business impact, risk assessment, and strategic alignment, rather than relying solely on p-values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.