What is statistical power?
PICTURE THIS: DATA SPLIT
Fit on train, tune on val, report on test once.
Simple meaning
Power is the probability of rejecting the null when a specified alternative is true, equal to one minus the Type II error rate.
WHY — Hypothesis Testing instead of guessing?
Why interviewers care about Hypothesis Testing:
people 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:
- 1Power is the probability
of rejecting the null when a specified alternative is true, equal to one minus the Type II error rate.
- 2It rises with sample
size, effect size, and lower variance, and it falls if you demand a smaller alpha.
- 3Underpowered tests waste traffic
and produce noisy, non-replicable wins.
- 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
Power is the probability of rejecting the null when a specified alternative is true, equal to one minus the Type II error rate. It rises with sample size, effect size, and lower variance, and it falls if you demand a smaller alpha.