The simplest way to think about it is that it’s the probability of a thing.
In common stats usage, it’s the probability that your null hypothesis is true. Most of the time, your null hypothesis boils down to the idea that two samples came from the same population. That is, that the two samples are not different. (More precisely that they have different means.) Typically, the whole reason you are doing the test is that you kinda suspect that your samples are different so you kinda hope that null hypothesis is wrong.
So the p-value is the probability of the thing you don’t want. That’s why people go looking for very low p values. Why .05 is the common cutoff? That’s a whole ‘nother issue.
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