What is stratified sampling, and when is it better than simple random sampling?
PICTURE THIS: 1, 2, 2, 8
Simple meaning
Stratified sampling draws separately within subgroups, such as country or plan tier, often in proportion to size.
WHY — Sampling instead of guessing?
Why interviewers care about Sampling:
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:
- 1Stratified sampling draws separately
within subgroups, such as country or plan tier, often in proportion to size.
- 2It guarantees representation of
small but important strata and can reduce variance of the overall estimate.
- 3It helps when strata
means differ and you care about each slice.
- 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
Stratified sampling draws separately within subgroups, such as country or plan tier, often in proportion to size. It guarantees representation of small but important strata and can reduce variance of the overall estimate.