A Type II error occurs when a statistical 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, one exists. This can lead to missed opportunities or incorrect assumptions in research and decision-making. For example, if a new medication is tested for effectiveness and the test concludes it does not work when it actually does, this is a Type II error. Such errors can have significant implications in fields like medicine, psychology, and social sciences, where accurate conclusions are crucial for progress.