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The significance level $\alpha$ controls the chance of rejecting a true null hypothesis. It defines the probability of making a Type I error, which is rejecting $H_0$ when it is actually true.

Conditions: The test is conducted under the null model.; $\alpha$ is the pre-specified significance level.

The significance level $\alpha$ controls the chance of rejecting a true null hypothesis. It defines the probability of making a Type I error, which is rejecting $H_0$ when it is actually true.

Conditions: The test is conducted under the null model.; $\alpha$ is the pre-specified significance level.

A one-tailed test allocates the entire significance level $\alpha$ to its designated tail. Unlike a two-tailed test which splits $\alpha$ into $\alpha/2$ for each tail, a one-tailed test places all of $\alpha$ in either the left or the right tail, depending on the direction specified by the alternative hypothesis.

Conditions: The test is one-tailed.; The significance level is $\alpha$.; The direction is specified by the alternative hypothesis ($<$ or $>$).

A one-tailed test allocates the entire significance level $\alpha$ to its designated tail. Unlike a two-tailed test which splits $\alpha$ into $\alpha/2$ for each tail, a one-tailed test places all of $\alpha$ in either the left or the right tail, depending on the direction specified by the alternative hypothesis.

Conditions: The test is one-tailed.; The significance level is $\alpha$.; The direction is specified by the alternative hypothesis ($<$ or $>$).