Significance level

Definition

The significance level, denoted by $\alpha$, is the probability of rejecting the null hypothesis when it is actually true. It represents the threshold for determining whether a result is statistically significant.

5 Must Know Facts For Your Next Test

1. Common significance levels are 0.05, 0.01, and 0.10.
2. A lower significance level means stricter criteria for rejecting the null hypothesis.
3. The significance level is chosen before conducting a hypothesis test.
4. If the p-value is less than or equal to $\alpha$, the null hypothesis is rejected.
5. Significance level controls the Type I error rate in hypothesis testing.

Review Questions

• What does a significance level of 0.05 mean in terms of Type I error?
• How do you determine whether to reject the null hypothesis using the p-value and significance level?
• Why is it important to choose a significance level before conducting a hypothesis test?

Related terms

Null Hypothesis:

A statement that there is no effect or no difference, which serves as a starting point for statistical testing.

Type I Error:

The error that occurs when rejecting a true null hypothesis; also known as a false positive.

p-value:

\text{The probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true.}