What would you like to understand?

In a symmetric two-tailed construction, the significance level $\alpha$ is split equally between the two tails. Each tail receives $\alpha/2$.

Conditions: The test is a symmetric two-tailed test.; The significance level is $\alpha$.

In a symmetric two-tailed construction, the significance level $\alpha$ is split equally between the two tails. Each tail receives $\alpha/2$.

Conditions: The test is a symmetric two-tailed test.; The significance level is $\alpha$.

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 $H_0$.

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 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.