A Type II Error occurs in statistical hypothesis testing when a test fails to reject a false null hypothesis. In simpler terms, it means that the test concludes there is no effect or difference when, in fact, there is one. This error can lead to missed opportunities or incorrect assumptions about a situation. For example, if a new medication is tested and the results suggest it is ineffective when it actually works, this is a Type II Error. Understanding this concept is crucial in fields like medicine, psychology, and quality control, where accurate decision-making is essential.