Hypothesis testing : p value method

p value can be interpreted as the probability that the null hypothesis is correct. So higher the p value, more is the probability of the H0 to be accepted.
p Value is the probability of the observed or even more extreme result assuming the null hypothesis is true. As shown in the diagram, a greater p value imply that the observed data point is closer to the mean of the population. That obviously implies the null hypothesis is true. Lesser p value implies null hypothesis is false.

This is a more direct method.
1. Just get the zscore of the mean of the sample taken, based on which we are planning to reject the null hypothesis.
2. Find the cumulative probability of the zscore using =NORM.S.DIST(zscore)
3. 1- cumulative freq = p value. This value is aligned in the same manner as the significance level or critical region. So you can directly compare both and decide whether your mean lies in the acceptance or rejection region.
4. Remember to double the p value if it is a dual tale problem.
*5. Note that you need not to double the significance level. It is already a cumulative one. If there is a two tailed problem, the significance level is already the double of each tail. So if the significance level is 0.05, then in each tail it is 0.025! So, you need not to double 0.05 again!!!
** In a way it is similar to critical value. In critical value method we try to spot the critical value’s z score and then actual interval x coordinate and see how far or near it is from the object of interest. In p-value method, we do not try to find the critical value’s value from probability. On the other hand we find the probability of the point of interest in pvalue method.