What should the distribution of p-values look like under a true null?
PICTURE THIS: 1, 2, 2, 8
Simple meaning
For a continuous test with a correctly specified null, p-values are uniform on 0 to 1.
WHY — P-value instead of guessing?
Why interviewers care about P-value:
who only read docs from people who shipped.
and tied to Data Science work.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1For a continuous test
with a correctly specified null, p-values are uniform on 0 to 1.
- 2A pile-up near 0
in a battery of null A/A tests signals a broken variance estimate, leakage, or peeking.
- 3A pile-up near 1
can mean discrete tests, conservative methods, or correlated one-sided mistakes.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
For a continuous test with a correctly specified null, p-values are uniform on 0 to 1. A pile-up near 0 in a battery of null A/A tests signals a broken variance estimate, leakage, or peeking.