A False Negative is a term commonly used in binary classification problems in statistics, machine learning, and diagnostic testing. It represents an error that occurs when a test result or a classification model wrongly indicates a negative outcome when the actual situation is positive.

In the context of a binary classification:

• True Positive (TP): Represents the cases where the test result or model correctly identifies a positive condition as positive.
• False Negative (FN): Represents the cases where the test result or model incorrectly identifies a positive condition as negative.

For example, let's consider a medical test for a disease. A False Negative in this scenario would occur when the test incorrectly indicates that a person does not have the disease when, in reality, the person does have the disease.

False Negatives are particularly critical in certain applications, such as medical diagnostics or security screenings, where missing a positive case (such as a disease or a security threat) can have significant consequences. In such cases, minimizing the occurrence of False Negatives is crucial, often at the expense of increasing the occurrence of False Positives (indicating a positive outcome when it's actually negative).

The concept of False Negatives is also relevant in various other fields, such as quality control, risk assessment, and information retrieval, where the accuracy of detecting positive instances is essential.