Sure. P-value is the universal* way of expressing statistical likelihood. It corresponds to a percentage: p=.05 just means 5%, and p=.001 means 0.1%, etc. It'

Sure. P-value is the universal* way of expressing statistical likelihood. It corresponds to a percentage: p=.05 just means 5%, and p=.001 means 0.1%, etc. It's often inaccurately explained as the likelihood of getting our results through chance alone. That's wrong for reasons that are technically important, but not in a way that really inhibits understanding of the strength of results that have small p-values. * It has flaws, and a growing number of researchers believe it should not have the prominent importance currently placed on it. ---- Stop reading here if you're already satisfied. ---- We want to measure if there's a difference between two groups. So, we take measurements of a portion of group A, and measurements of a portion of group B. Mathematically, we assume that what we've actually done is sampled from the same population both times. If that's true, our data sets should be quite similar to one another, but of course there will be some difference just due to noise. So, we compute mathematically the chance of seeing a difference at least as large as the one we see between our data sets, if that assumption is true. We decide beforehand on a small error rate. If not otherwise stated, 5% (p=.05) is the universal standard. If we find that the likelihood of observing a same-or-larger difference between the populations is smaller than that already-small 5% chance, we REJECT our assumption that the samples came from the same population, and conclude that the populations must actually be different. In other words, we conclude that we measured an actual difference that contrasts two mathematically-separate populations.