Rejection region or p-value I am writing a research paper where I am using an hypothesis test.
Is it better to give a p-value for this test or use a 5% two-tailed rejection region?
Thanks in advance!
 A: In situations like these - it's best to look at things from the reader's perspective. Would the reader care about the actual value of the test statistic? Do you want the reader to know that the $T$-statistic is $2.79$ or $F = 8.91$? In most cases, the reader would not be interested in these values, so just give the p-value along with the test that you used and an estimate of the magnitude of your effect size.
A: I suggest you to put a exact p-value for the testing you've done. However if the p-value is very small, e.g. 0.000001, then I would write it as p-value < 0.0001. 
Hope this helps. 
A: I would recommend to report $p$-values and if you have the space go for the test statistics and rejection region. But there is a reason behind that as in the most research journals and software you can see $p$-values. First of all, let me say that the conclusions based on $p$-values and rejection region approach is basically the same i.e. if your $p$-values is less that or equal to $\alpha$ (i.e. your significance level) then the test statistic will fall into the rejection region and vice versa. The rejection region method only provides a decision for a specific value of $\alpha$. However by providing the $p$-values, your reader not only can make his/her decision based on that particular $\alpha$ level but also he/she can see all possible values for $\alpha$ in which the null hypothesis can be rejected. As a result, the $p$-value provides more flexibility than the rejection region method.   
You can also have a look at the last paragraph in page 449 of "Statistical Ideas and Methods" by Jessica Utts, Robert Heckard here that addresses this property.
