Unless clinical tests are applied and interpreted carefully, they can be very misleading (Kaneko, Kuwana, Kameda, & Takeuchi, 2011). Often, the basic premises of a diagnostic test includes two populations of people i.e. those with the disease in question and those individuals without the underlying medical condition who differ from the other group on at least a testable parameter. An example is patients who have pneumonia infiltrates on an x-ray while the control group without pneumonia does not.

When conducting a screening or a diagnostic test, there are two obvious errors that are the test can fail to identify persons who do not have a condition or it could as well falsely classify a person as having the pathological condition when they do not actually have it. It is important to note that if you adjust some threshold value in an effort of reducing one type of error, the other type of error increases. In explaining the phenomenon better, the sensitivity and specificity concepts are used in determining the accuracy of a test.

According to Donner-Banzhoff (2011), the sensitivity also referred to as the recall or true positive rate measures the actual proportion of true positives correctly identified as suffering from the disease. Thus, the sensitivity will refer to the probability that the test will turn out positive in patients known to be suffering from the condition. A test can either have a high or low sensitivity. Highly sensitive tests have&nbsp.lesser negative results and are very accurate and useful in ruling out a certain disease being tested in a population (Smith et al., 2011). An example is when a test is 96 percent sensitive, then 96 percent of the patients with the pathological condition will provide true positive results while the other 4 percent will provide false-negative results.

Highly sensitive tests are important when ruling out dangerous pathological conditions such as lumbar puncture for subarachnoid hemorrhage. On the other hand, specificity also referred to as true negative rate determines the proportion of negatives that is the proportion of healthy persons who are identified correctly as not suffering from the pathological condition being tested. It is the probability that a test will turn out to be negative in patients who are disease-free in the population being tested.

As well, the test has high or low specificity. A highly specific test has fewer positives results and is significant and useful in ruling out the disease in a test. An example is that when a test is 96 percent specific, it means that 96 percent of the patients will produce true negative results and lack the disorder while the rest 4 percent are false positives and have the pathological disorder. A specific test is important in situations where the false positive test could result in harm (Donner-Banzhoff, 2011).

An example when a specific test is used is when therapy is potentially dangerous in long-term anticoagulation therapy in determining the accuracy of the procedure. Both sensitivity and specificity are important in determining the accuracy of a test that is the overall test performance in patients with or without the disease.

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