A/B Test Interpretation Questions
Part of the Data Analyst Roadmap
In short
"The test shows a 5% lift. Should we ship?" The interviewer is waiting to see if you'll ask the right questions before saying yes.
'Here are the results of a test, what would you conclude?' — testing whether you can read significance, effect size and guardrails correctly rather than just checking a p-value box.
Think of it like this
The new ice cream flavour
An ice cream parlour launches a mango flavour and sales jump. But it was also the hottest week of May. Was it the flavour or the heat? A good A/B test answers that by giving both flavours on the same days, and you still need to read it carefully.
Watch it happen
Questions to ask before saying "ship it"
1/6Was the traffic split as planned? A lopsided split can mean a bug.
The big ideas
What you need to know
Statistical vs practical
A result can be real but too small to matter. Always ask what the lift is worth in rupees or users.
Watch for peeking
Checking results daily and stopping on the first good day inflates false wins.
Novelty effect
Users click new things just because they're new. The lift can fade after a week or two.
Segments can mislead
Slicing results into many segments will find some "win" by chance. Treat surprising segment results as ideas to retest.
Save yourself the pain
Common mistakes
Oops
Saying "p < 0.05, ship it" without asking anything else.
Do this instead
Check the split, effect size, guardrails and duration before recommending.
Oops
Celebrating a big win in one small segment found after the test.
Do this instead
Call it a hypothesis and suggest a new test focused on that segment.
Your mission
Interpret three made-up results
Quick check
A test shows +0.2% conversion, statistically significant, but the feature needs three months of engineering. What's the best response?
Go deeper
- 1Review Phase 10 and practice interpreting a sample experiment readout out loud.
- 2Prepare to discuss what you'd want to check before trusting a surprising result.
- 3Practice giving a clear recommendation, not just a statistical description.
More statistics & analytical at this stop
Statistics & Probability Questions
Case Study / Guesstimate Questions
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