How is a Type II error defined?

A Type II error occurs when a researcher fails to reject a null hypothesis that is actually false. Essentially, this means that the hypothesis test concludes that there is not enough evidence to support a significant effect or difference when, in reality, one exists.

In the context of hypothesis testing, the null hypothesis typically posits that there is no effect or difference. If the null hypothesis is false and the researcher does not identify this—resulting in the retention of the null hypothesis—the situation describes a Type II error.

This is important in statistical analysis, particularly when assessing the power of a test, which refers to its ability to correctly reject a false null hypothesis. Understanding Type II errors helps researchers recognize the risks associated with failing to detect true effects in their data.