Six Sigma Green Belt Certification Practice Exam 2025 - Free Six Sigma Practice Questions and Study Guide

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.