Actually, p-values are about CORRELATION.
Maybe *you* aren't well-positioned to be denigrating others as not statistical experts.
I may be responding to a troll here, but, no, the GP is correct. P-values are about probability. They're often used in the context of evaluating a correlation, but they needn't be. Specifically, p-values specify the probability that the observed statistical result (which may be a correlation) could be a result of random selection of a particularly bad sample. Good sampling techniques can't eliminate the possibility that your random sample just happens to be non-representative, and the p value measures the probability that this has happened. A p value of 0.05 means that there's a 5% chance that your results are bogus in this particular way.
The problem with p values is that they only describe one way that the experiment could have gone wrong, but people interpret them to mean overall confidence -- or, even worse -- significance of the result, when they really only describe confidence that the sample wasn't biased due to bad luck in random sampling. It could have been biased because the sampling methodology wasn't good. I could have been meaningless because it finds an effect which is real, but negligibly small. It be meaningless because the experiment was just badly constructed and didn't measure what it thought it was measuring. There could be lots and lots of other problems.
There's nothing inherently wrong with p values, but people tend to believe they mean far more than they do.