Final answer:
A false negative is when a test incorrectly predicts no disease when the disease is present, and the correct answer to the student's question is C: The predicted result was negative, and the actual result was positive. Tests with high sensitivity have fewer false negatives, and high specificity relates to lower false positives.
Step-by-step explanation:
A false negative result in a predictive model or diagnostic test occurs when the test predicts a negative result (e.g., no disease or condition) but in reality, the individual does have the disease or condition. To answer the student's question directly: the correct definition of a false negative result is C: The predicted result was negative, and the actual result was positive, meaning the test inaccurately reported the absence of a condition when it was actually present.
It is important to consider test sensitivity and test specificity when evaluating the performance of a diagnostic test. High sensitivity means fewer false negatives, while high specificity means fewer false positives. The balance between these two aspects determines the effectiveness of a test.
Moreover, every predictive model and medical test carries the risk of erroneous predictions, such as false negatives and false positives, which can be influenced by factors like cross-reactivity or low test quality. Understanding these risks can help in interpreting test results and deciding on further confirmatory testing.