A "True Negative" is a term used in statistics and machine learning to describe a situation where a test correctly identifies the absence of a condition. For example, if a medical test is conducted to check for a disease and the result is negative, but the person does not have the disease, this outcome is considered a true negative. It indicates that the test has accurately ruled out the condition. True negatives are important for evaluating the performance of diagnostic tests. They help determine the test's specificity, which measures how well the test identifies those without the condition. High true negative rates contribute to the reliability of tests in fields like medicine and data science.