Why is a p-value not a posterior probability that the variant is better?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
A p-value conditions on the null and does not use a prior over effect sizes.
WHY — P-value instead of guessing?
Why interviewers care about P-value:
on P-value.
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:
- 1A p-value conditions on
the null and does not use a prior over effect sizes.
- 2Bayesian decision rules compare
posterior probabilities or expected loss, which can disagree with p less than 0.05 for the same data.
- 3Mixing the two languages
is how teams overclaim certainty after a noisy win.
- 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
A p-value conditions on the null and does not use a prior over effect sizes. Bayesian decision rules compare posterior probabilities or expected loss, which can disagree with p less than 0.05 for the same data.