What is p-hacking and why is it dangerous?
PICTURE THIS: DATA SPLIT
Fit on train, tune on val, report on test once.
Simple meaning
Running many tests until one looks significant.
WHY — Stats instead of guessing?
Why interviewers care about Stats:
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:
- 1Running many tests until
one looks significant.
- 2Why it exists
False discoveries mislead product decisions.
- 3Pre-register hypotheses when stakes
are high.
- 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
Running many tests until one looks significant. False discoveries mislead product decisions.