What is p-hacking?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
P-hacking is trying many analyses, metrics, segments, or stopping times until a significant p-value appears, then reporting that path as if it were planned.
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:
- 1P-hacking is trying many
analyses, metrics, segments, or stopping times until a significant p-value appears, then reporting that path as if it were planned.
- 2It makes published p-values
too small and results hard to replicate.
- 3Pre-specify the primary metric
and analysis, and treat extra cuts as exploratory.
- 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
P-hacking is trying many analyses, metrics, segments, or stopping times until a significant p-value appears, then reporting that path as if it were planned. It makes published p-values too small and results hard to replicate.