A p value is the probability of seeing data at least as extreme as yours if the null hypothesis were true. It is not the probability that the null is true, and it is not the probability your result is real. A p of 0.03 means data this extreme would appear three percent of the time in a world with no effect.
Why interviewers ask this
Misreading a p value is the most common way a data scientist ships a wrong decision, and the misreading usually happens out loud in a meeting. The interviewer is testing whether you hold the definition precisely under pressure and whether you can translate it without either lying to the stakeholder or drowning them. They often follow up on what a confidence interval adds, so have that ready.
How to structure your answer
- State the formal definition in one clean sentence.
- Say explicitly what it is not, since that is the misconception in the room.
- Translate it into a sentence a product manager can repeat.
- Point to the effect size and interval as the numbers they should act on.
Example answer
I would say it like this: the p value answers one narrow question, which is how surprising this data would be if the feature did nothing at all. If it is 0.03, then in a world where the change had zero effect, we would still see a swing this big about three times in a hundred. That is all it says. It is not a three percent chance the null is true, and it is definitely not a ninety seven percent chance the feature works. Then I move them off it, because on its own it is a bad decision tool. I show the effect size and the confidence interval instead: the lift is 1.2 percent, plausible range 0.1 to 2.3 percent. That reframes the conversation from is it real to is the smallest plausible version of this worth building. I have had launches where p was under 0.05 and the interval still included a lift too small to pay for the engineering, and that is the conversation actually worth having.
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See how it worksFollow-up questions to expect
- What does a 95 percent confidence interval actually mean, then?
- If we run the same test twice and get p equals 0.04 and p equals 0.06, what do you conclude?
- How would you explain statistical power to the same audience?
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