What is the primary difference between Type 1 and Type 2 errors in hypothesis testing?

The primary distinction between Type 1 and Type 2 errors in hypothesis testing centers on the nature of the errors themselves. A Type 1 error occurs when the null hypothesis is incorrectly rejected when it is actually true, which is commonly referred to as a "false positive." This means that the test indicates there is an effect or a difference when, in reality, there is none.

Conversely, a Type 2 error occurs when the null hypothesis is not rejected when it is actually false, also known as a "false negative." In this case, the test fails to detect an effect or a difference that is truly present.

Understanding these definitions is crucial in statistical hypothesis testing, as they highlight the risks associated with drawing conclusions from data. Type 1 errors carry the risk of labelling a nonexistent effect as real, while Type 2 errors risk missing a genuine effect. This distinction is vital for researchers to design studies appropriately and interpret results accurately.