What is a minimum detectable effect (MDE)?
PICTURE THIS: DATA SPLIT
Fit on train, tune on val, report on test once.
Simple meaning
MDE is the smallest true effect the test is designed to detect with the planned power, alpha, and sample size.
WHY — A/B Testing instead of guessing?
Why interviewers care about A/B 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:
- 1MDE is the smallest
true effect the test is designed to detect with the planned power, alpha, and sample size.
- 2It is a design
choice about what is worth detecting, not a property of the data after the fact.
- 3If MDE is larger
than any lift you would ship, the test is the wrong investment.
- 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
MDE is the smallest true effect the test is designed to detect with the planned power, alpha, and sample size. It is a design choice about what is worth detecting, not a property of the data after the fact.