False positives and negatives are errors in statistical hypothesis testing and decision-making processes where a test incorrectly indicates the presence or absence of a condition. These errors can significantly impact the reliability of diagnostic tests, algorithms, and systems, necessitating careful consideration of their rates and consequences in design and evaluation.