I am doing hypothesis testing using t-test and p-value. I calculate my p-value based on the degrees of freedom and the t-test value. The p-value is a two-tailed calculation. But I am not very sure what the difference would be if I do it with one-tailed p-value. What is the meaning of that? How should I interpret that?
Right now I have around 18 to 25 degrees of freedom, depending on the test, and I may get values of p around 0.001-0.065 for a two-tailed calculation. I reject the test if I get more than 0.05 for the two-tailed calculation. Is that reasonable?
fed1, thanks for your response. I'm following most literature I have found about p-value interpretation. Here is one from Wikipedia. From there that I am rejecting my null hypothesis when more than 0.05.
What is not very clear to me is the one or two-sided p-value and when to use one or the other.
Depends whether the hypothesis you're testing is one or two tailed.
The one tailed p table is appropriate for testing one tailed hypotheses and the two tailed p table is for two tailed hypotheses. You may need to skip back to the hypothesis section of your texts to sort it out.
Not that I'm any sort of expert.
As for two-tailed vs one-tailed test:
if we think to t-test as a means to ascertain the statistical significance of the difference between two samples' mean, the two-tailed version is appropriate when we have no expectations about the direction of that difference.
The opposite holds true for the one-tailed version.
I think it is reject null hypothesis if p value is less than 0.05 (Alpha=.05)
Yes, you got it.
thanks! yeah, I had found this other one that I think is more explicit:
differences between one-tailed and two-tailed tests
It is from a very good source too. So, highly recommended for those interested
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