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, there is one. This can lead to missed opportunities or incorrect assumptions in research and decision-making. The probability of making a Type II Error is denoted by the symbol β (beta). Factors such as sample size, effect size, and significance level can influence the likelihood of this error. Researchers aim to minimize Type II Errors to ensure their findings are accurate and reliable.