I would discussion point 1 in page 340. I used to think the P value showed the chance that the null hypothesis (a default assumption that there’s no effect or difference) might be true just by random luck. For example, I believed a P value of 0.05 meant the null hypothesis could randomly happen 5% of the time. But, I got it wrong. What the P value really does is; if we assume the null hypothesis is right, how likely it is to see the data we collected . So, when we get a P value of 0.05, it doesn’t tell us about the 5% chance of the null hypothesis itself being true. Instead, it means there’s a 5% chance of finding data like ours if the null hypothesis were true. It’s basically saying, ‘Given the null hypothesis is true, our data seem pretty unlikely. It’s about the data’s fit with the null hypothesis, not the hypothesis’s own likelihood.