Type I

Definition

A Type I error occurs when a true null hypothesis is incorrectly rejected. It is also known as a false positive error.

5 Must Know Facts For Your Next Test

1. The probability of committing a Type I error is denoted by $\alpha$ (alpha).
2. $\alpha$ is often set at 0.05, meaning there is a 5% risk of rejecting the null hypothesis when it is actually true.
3. Reducing $\alpha$ decreases the likelihood of a Type I error but increases the risk of a Type II error.
4. Type I errors are considered more serious in many contexts because they imply finding an effect or difference that does not exist.
5. In hypothesis testing, controlling for Type I errors involves setting an appropriate level of significance ($\alpha$) before conducting the test.

Review Questions

• What does a Type I error signify in hypothesis testing?
• How is the probability of making a Type I error represented and typically set?
• Why might reducing $\alpha$ increase the risk of committing another type of error?

Related terms

Null Hypothesis:

A statement that there is no effect or no difference, which researchers aim to test against an alternative hypothesis.

$\alpha$ (Alpha) Level: The threshold p-value that determines when to reject the null hypothesis, commonly set at 0.05.

Type II Error:

Occurs when a false null hypothesis fails to be rejected; also known as a false negative error.