When should you prefer a nonparametric test or a bootstrap over a classical parametric test?
PICTURE THIS: 1, 2, 2, 8
Simple meaning
Prefer them when the estimand is a median or quantile, the metric is skewed with small n, or you distrust the analytic variance.
WHY — Hypothesis Testing instead of guessing?
Why interviewers care about Hypothesis Testing:
on Hypothesis Testing.
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:
- 1Prefer them when the
estimand is a median or quantile, the metric is skewed with small n, or you distrust the analytic variance.
- 2Nonparametric tests have weaker
distributional assumptions but can be less powerful when the parametric model is true.
- 3A carefully blocked bootstrap
can also handle clustering that a textbook t-test ignores.
- 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
Prefer them when the estimand is a median or quantile, the metric is skewed with small n, or you distrust the analytic variance. Nonparametric tests have weaker distributional assumptions but can be less powerful when the parametric model is true.