Type I and Type II Errors in Hypothesis Testing
You are discussing hypothesis testing in the context of a modeling or experimentation project.
Define Type I and Type II errors, explain the difference between them, provide real-world examples, and describe how you would manage the trade-off in an A/B test.
Constraints & Assumptions
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Define the null and alternative hypotheses clearly.
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Connect Type I error to false positives and Type II error to false negatives.
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Discuss alpha, beta, power, sample size, effect size, and business cost.
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Avoid saying that a p-value is the probability the null hypothesis is true.
Clarifying Questions to Ask
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What is the decision being made from the hypothesis test?
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Which mistake is more costly: launching a harmful change or missing a beneficial change?
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What minimum detectable effect matters to the business?
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Is the test one-sided or two-sided?
What a Strong Answer Covers
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Defines Type I error as rejecting a true null hypothesis and Type II error as failing to reject a false null hypothesis.
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Explains alpha as the Type I error rate and beta as the Type II error rate, with power equal to one minus beta.
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Gives practical examples such as a false experiment win versus missing a real product lift.
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Explains the trade-off between alpha, power, sample size, test duration, variance, and minimum detectable effect.
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Recommends choosing thresholds based on business risk, not habit alone.
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Mentions multiple testing, guardrails, and practical significance.
Follow-up Questions
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How would you explain Type I and Type II errors to a product manager?
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What happens to power if the effect size is smaller than expected?
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When would you use a stricter alpha than 0.05?