I would like to present point 9 on page 341, “the P value is the chance of our data occurring if the test hypothesis is true”.
This is also a common misinterpretation about P values in hypothesis testing as the direct translation of 0.05 P value to 5% chance of observing the data if the null hypothesis is true. In my understanding, the P value doesn’t only consider the observed data but also includes observations of more extreme than what was observed.
The P value actually will refer to data frequency when all assumptions used to compute it are correct, and a low p-value (like 0.05) suggests the data is unlikely due to chance alone because it falls outside the range expected under the null hypothesis. The accuracy of the p-value will also depend on the validity of the assumptions used for the test like taking random sampling, no manipulating to get a desired P value, and no missing or biased data.