So after doing some research, people tend to confuse as to what the P-value actually stands for and results in wrong interpretation of examples. Take this one:
Prof. Johnson conducts a hypothesis test on whether the proportion of all UBC students who bike to school equals 30%. Specifically, Prof . Johnson has H0 : p = 0.3 versus HA : p ≠ 0.3 .
He obtains a P-value of 0.01. On the other hand, Prof . Smith would like to test if there is sufficient evidence to support that p is greater than 0.3 at the 10% significance level. Based on Prof. Johnson's result, will the null hypothesis of Prof. Smith's test be rejected?
So I'm not sure if this P-value in this context justifies rejecting the null hypothesis because we don't know the direction of the P-value no? I understand the P-value should be cut in half to accommodate the 1-tailed test in this case
Any help is appreciated.