I choose the first one which I like the most from the paper “The P value is the probability that the test hypothesis is true, No” from page 341 as most of of us (researchers/ public health specialist) ever say like it is statistically significant due to p value is less than 0.05. Most of us (including me) mistakenly think that the P value tells us the chance that the hypothesis is correct for example like if P value is 0.01 we assume that there is only a 1% chance the hypothesis is true. But this is not correct. The correct one is about the P value is calculated under the assumption that the hypothesis is already true. This show us different the data is from expectation if the hypothesis is true. So, P values do not give the probability that the hypothesis is right or wrong. They only measure how well the data inline with what we expected under the hypothesis.