the discrete case, if I regard an observed 8 heads and 2 tails as biased, I would also so regard 9 heads and 1 tail or 8 tails one 1 head. So we actually mean "the probability of data this extreme". What that means is not always obvious. If we're rolling dice rather than tossing coins, is zero 1s, three 2s and six 3s more extreme than five 1s and five 3s? That's where we have to introduce the concept of a statistic - a single number which encapsulates the relevant aspect of the data.] I might be wrong, but I've thought the p-value meant the probability of getting a result given that the null hypothesis is true. So when you get say p = .03 for example, having set the alpha level at .05, you could say, that you are confident enough to rule out the null hypothesis (by saying that if the null hypothesis is true in reality, you got bloody lucky) ,whereas if you get a p =.06, you could only say that you just can't rule out that the null hypothesis (but not that you are ruling in the null hypothesis). As to the frequentist vs Bayesian debate, that could fill up an article in itself! 11 months ago Derek Bolton Retired s/w engineer (logged in via email @gmail.com) David, your description of p value is correct. The problem arises in the next step: "confident enough to rule out the null hypothesis". In saying that, you