Why should you report effect size along with a p-value?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
A tiny effect can be highly significant in a huge sample, and a large effect can miss significance in a small sample.
WHY — P-value instead of guessing?
Why interviewers care about P-value:
on P-value.
the situation, the default choice, and one exception - that reads as experience.
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:
- 1A tiny effect can
be highly significant in a huge sample, and a large effect can miss significance in a small sample.
- 2Effect size and a
confidence interval answer how big the change is and how precisely you know it.
- 3Shipping decisions need practical
magnitude, not stars on a p-value.
- 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
A tiny effect can be highly significant in a huge sample, and a large effect can miss significance in a small sample. Effect size and a confidence interval answer how big the change is and how precisely you know it.