Statistical Significance Without Jargon: An Accurate Everyday Account of P-Values
Company: Unity
Role: Product Manager
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Other
You are interviewing for a product manager role and are asked to explain a p-value to someone who has never worked with data. The interviewer suggests a concrete listener: imagine you are explaining it to your grandmother. Give the explanation you would actually say out loud, keep it free of jargon, and make sure the simple version is still correct rather than merely reassuring.
```hint Begin with luck
Pick an everyday situation where pure chance could produce a result that looks suspicious. Then ask what you would have to assume about that situation before you could say how surprising the result is.
```
### Clarifying Questions
- Is the listener simply curious, or will they use the explanation to act on something, such as approving a change after a test?
- May I use a tiny worked example with numbers, or should the explanation be entirely verbal?
- Should I also cover the usual 0.05 cutoff and what it means when a result does not reach it, or only the core idea?
### What a Strong Answer Covers
- An everyday analogy where chance alone could create a surprising outcome, with the "suppose nothing unusual is going on" starting point stated openly.
- A plain-language definition that remains technically right: how often a result at least this extreme would turn up if there were no real effect, which is not the probability that the idea under test is true.
- What a small value and a large value each allow you to conclude, including why a large value does not prove that two options perform the same.
- Where the cutoff comes from, as a choice about how many false alarms to tolerate, and the difference between a statistically significant result and a large or valuable one.
- Checking that the listener understood and adjusting the explanation, instead of reciting a definition once.
### Follow-up Questions
- After a test, a stakeholder hears "p = 0.03" and concludes there is a 97 percent chance the new design is better. How do you correct this without losing them?
- A test ends with p = 0.20 and the team wants to announce that the change had no effect. What do you tell them?
- An analyst checked 20 metrics and is excited about the one that crossed 0.05. Why should that make you cautious?
Overview: A product manager interview prompt on explaining a p-value to a listener with no statistics background, such as a grandparent. It tests whether the candidate can build an everyday analogy that stays accurate, separate statistical significance from effect size, and avoid the common misreadings of A/B test results.