Six Sigma Green Belt Certification Practice Exam

Which option refers to a Type I error?

1. Rejecting the null hypothesis when it is true

2. Accepting the null hypothesis when it is true

3. Not rejecting the null hypothesis when it is false

4. Accepting the null hypothesis when it is false

The correct answer is: Rejecting the null hypothesis when it is true

A Type I error occurs when a null hypothesis is incorrectly rejected when it is actually true. In hypothesis testing, the null hypothesis represents a default position or statement that there is no effect or no difference in the population being studied. When researchers conduct statistical tests, they set a significance level (alpha), which defines the threshold for deciding whether to reject or fail to reject the null hypothesis. A Type I error signifies a false positive, meaning that the test has indicated a significant effect or difference exists when it does not. In contrast, the other scenarios—accepting a true null hypothesis, not rejecting a false null hypothesis, or accepting a false null hypothesis—deal with different concepts associated with hypothesis testing. Accepting a true null hypothesis is the desired outcome, while not rejecting a false null hypothesis leads to a Type II error, which deals with failing to detect a true effect. Understanding these distinctions is crucial for effectively interpreting statistical results and making informed decisions based on data analysis.