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In hypothesis testing, alternative hypothesis doesn't have to be the opposite of null hypothesis. For example, for $H_0: \mu=0$, $H_a$ is allowed to be $\mu>1$, or $\mu=1$. My question: Why is this allowed? What if in reality, $\mu=-1$ or $\mu=2$, in which case if one applies, say, likelihood ratio test, one may (wrongly) conclude that $H_0$ is accepted, or $H_0$ is rejected and hence $H_a$ is accepted?
What about this proposal: $H_a$ should always be the opposite of $H_0$? That is, $H_a: H_0$ is not true. This way, we are effectively testing only a single hypothesis $H_0$, rejecting it if the p-value is below a predefined significance level, and not have to test two hypotheses at the same time that can be both wrong.