Level of significance

Definition

Level of significance, denoted as $\alpha$, is the threshold for determining whether a null hypothesis should be rejected. It represents the probability of making a Type I error, which is rejecting a true null hypothesis.

5 Must Know Facts For Your Next Test

1. Common levels of significance are 0.05, 0.01, and 0.10.
2. If the p-value is less than or equal to $\alpha$, the null hypothesis is rejected.
3. A lower $\alpha$ reduces the chance of a Type I error but increases the chance of a Type II error.
4. The level of significance is chosen before conducting the hypothesis test to avoid bias.
5. $\alpha$ represents the area in the tails of the distribution that corresponds to extreme values.

Review Questions

• What does a level of significance represent in hypothesis testing?
• How do you decide whether to reject the null hypothesis based on $\alpha$ and p-value?
• Why might you choose an $\alpha$ value different from 0.05?

Related terms

Null Hypothesis (H₀): A statement that there is no effect or no difference, often representing a status quo or baseline scenario.

Type I Error: The error made when rejecting a true null hypothesis; also known as a false positive.

P-value: The probability, under the null hypothesis, of obtaining a result equal to or more extreme than what was actually observed.