P-value Explained Simply for Data Scientists

Recently, I got asked about how to explain p-values in simple terms to a layperson. I found that it is hard to do that.

P-Values are always a headache to explain even to someone who knows about them let alone someone who doesn’t understand statistics.

I went to Wikipedia to find something and here is the definition:

In statistical hypothesis testing, the p-value or probability value is, for a given statistical model, the probability that, when the null hypothesis is true, the statistical summary (such as the sample mean difference between two groups) would be equal to, or more extreme than, the actual observed results.

And my first thought was that might be they have written it like this so that nobody could understand it. The problem here lies with a lot of terminology and language that statisticians enjoy to employ.

This post is about explaining p-values in an easy to understand way without all that pretentiousness of statisticians.