Explain what a p-value is to a product manager, and one common way it is misunderstood.

What the interviewer wants to hear

• Probability of data this extreme if there is no real effect
• Small p-value means the result would be surprising under the null
• Not the probability that the null is true
• Does not measure effect size or importance

Model answer

I would say: imagine the new feature truly makes no difference. The p-value tells us how often we would see a result at least this big just from random chance in that world. If the p-value is 0.02, results like ours would only happen about 2% of the time by luck, so we have reasonable evidence of a real effect. A common misunderstanding is reading it as "there is a 2% chance the feature does nothing" or "98% chance it works". That is not what it means. Another mistake is treating a small p-value as a big effect. With huge samples, tiny and unimportant differences can be significant. So I always report the effect size with a confidence interval alongside the p-value, and focus on whether the change matters for the business.

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