I'm wondering what role t-statistic plays in hypothesis testing and the interpretation of the result.
In a two-sample two-tailed t-test where we have $H_0$: $\mu_A=\mu_B$ and $H_1$: $\mu_A \neq \mu_B$, I'm wondering the difference between the interpretation of the following two cases.
- Case 1: $p<\alpha$ and $t<0$
- Case 2: $p<\alpha$ and $t>0$
Given that $p<\alpha$, we reject $H_0$ and accept $H_1$ that $\mu_A \neq\mu_B$ in both cases, but how does the positivity or negativity of $t$ influence our interpretations of the two cases?
I have the same question for the one-tailed t test. Say that now we have $H_0$: $\mu_A\leq\mu_B$ and $H_1$: $\mu_A > \mu_B$ and we also consider the following two cases:
- Case 1: $p/2<\alpha$ and $t<0$
- Case 2: $p/2<\alpha$ and $t>0$
Then, how does the positivity/negativity of $t$ influence the two cases above in the one-tailed t-test?