A false positive is a form of reporting mistake that occurs in binary classification and medical testing when a test reveals the existence of a condition when it should not. In contrast, a false negative occurs when a test wrongly indicates the absence of a condition when, in fact, the condition is present (Muchinsky & Howes, 2019). It should be noted that a false positive result is the most negative compared to a false negative. The reason lies in the worst-case scenario when treating a disease. False-negative findings seem considerably worse than false-positive ones because of the potential consequences of not treating a cancer patient.
False-negative and false-positive results are possible with every diagnostic procedure. Due to the inherent limitations of medical testing, both false negative and false positive results are perennial concerns. Unnecessary treatment may come from a false positive, while a false negative might cause someone to overlook a potentially life-threatening illness (Muchinsky & Howes, 2019). Further data collection, consideration of other factors, and adjustments to the tests’ sensitivity and specificity may help reduce the likelihood of a mistake. As an additional method, repeating the tests may help reduce the number of false positives. Still, this is very difficult to do since the improvement in one area usually results in deterioration in another. However, there are situations where one kind of mistake is better than another.
Reference
Muchinsky, P. M., & Howes, S. (2019). Psychology applied to work: An introduction to industrial and organizational psychology. Hypergraphic Press, Inc.