Easy A/B Testing Question 21 of 220

Why is it risky to peek at A/B results every day and stop when they look significant?

Data Science track · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: DATA SPLIT

Train 70%Val 15%Test 15%

Fit on train, tune on val, report on test once.

Simple meaning

Repeated looks inflate the Type I error because you get many chances to cross the significance threshold by luck.

WHY — A/B Testing instead of guessing?

Why interviewers care about A/B Testing:

They are checking judgment

on A/B Testing.

A good answer names

the situation, the default choice, and one exception - that reads as experience.

Stay structured

Name the idea, why it exists, then one short example.

Close cleanly

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:

1
Repeated looks inflate the
Type I error because you get many chances to cross the significance threshold by luck.
2
Optional stopping without a
sequential design makes p-values too small.
3
Decide sample size or
a sequential method in advance, then stop according to that plan.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line

“Optional stopping without a sequential design makes p-values too small.”

Break into beats

Optionalstoppingwithoutasequentialdesign

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

Repeated looks inflate the Type I error because you get many chances to cross the significance threshold by luck. Optional stopping without a sequential design makes p-values too small.