How is the significance level allocated in a symmetric two-tailed test?

The form of the alternative hypothesis dictates the tail(s) of the test. $H_1:\mu\ne23$ gives a two-tailed test, $H_1:\mu<23$ a left-tailed test, and $H_1:\mu>23$ a right-tailed test.

Conditions: The null hypothesis is based on a population mean of 23.; The alternative hypothesis is formulated before seeing the data.

Failing to reject the null hypothesis means that the sample provides insufficient evidence against it. It is not a proof that the null is true; it simply indicates that the data do not contradict the null hypothesis strongly enough to warrant rejection.

Conditions: A hypothesis test has been conducted.; The decision is to fail to reject $H_0$.

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 null hypothesis includes the boundary value. In the example with a population mean of 23, the null hypothesis is centered around or includes this specific value as the reference claim against which the alternative hypothesis tests for departure.

Conditions: The example uses a population mean of 23.; The null hypothesis is formulated to include this value.

The null hypothesis serves as the reference claim that includes the boundary value. In the example, it represents the status quo or the specific departure being tested against the alternative hypothesis.

Conditions: The population mean is 23.; The null hypothesis includes the boundary value.