A p value is the probability of observing a result at least as extreme as yours if the null hypothesis were true. It does not give the probability that the null hypothesis is true, and it does not tell you the effect is important. A small p value with a tiny effect size on a huge sample is statistically significant and often commercially meaningless, so always report the effect size and confidence interval.
Why interviewers ask this
Misinterpreting p values is the most common statistical error in business analytics, and it leads directly to bad launch decisions. Interviewers want the conditional direction stated correctly, the separation of statistical from practical significance, and ideally a mention of confidence intervals as the more useful thing to report to stakeholders.
How to structure your answer
- State the definition with the conditional in the right direction.
- Explicitly correct the common misreading.
- Separate statistical significance from practical importance.
- Recommend reporting effect size and confidence interval instead.
- Mention multiple comparisons if you test many metrics.
Example answer
A p value is the probability of seeing data at least this extreme assuming the null hypothesis is true. The direction matters, because the way it usually gets misread is as the probability the null is true, or worse, the probability the test worked, and those are different statements entirely. What it definitely does not tell you is whether the result matters. On a large enough sample almost anything reaches significance. I had a test where a button change was significant at p below 0.01 and the actual lift was 0.1% on a metric with a wide confidence interval that included values too small to pay for the engineering work. So what I present to stakeholders is the effect size with a confidence interval, and I frame it as our best estimate is a 2% lift, plausibly between 0.5% and 3.5%. That gives people something they can weigh against cost. I also watch for multiple comparisons, since testing fifteen metrics guarantees a couple of false positives.
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See how it worksFollow-up questions to expect
- How would you correct for testing many metrics at once?
- What is a confidence interval, in plain language, for a stakeholder?
- What does a non significant result let you conclude?
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