Name a common misinterpretation of the p-value?
PICTURE THIS: DJANGO MVT
Simple meaning
People treat p as the probability the null is true, or as the probability the result is a fluke in an informal sense.
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:
- 1People treat p as
the probability the null is true, or as the probability the result is a fluke in an informal sense.
- 2They also treat p
greater than 0.05 as proof of no effect.
- 3Both mistakes ignore that
p is a tail probability computed assuming the null, not a posterior on hypotheses.
- 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
People treat p as the probability the null is true, or as the probability the result is a fluke in an informal sense. They also treat p greater than 0.05 as proof of no effect.