No diagnostic test is perfect. Some tests are very sensitive - they are good at detecting people who have a disorder. Classic examples include the D-dimer which is often positive in individuals with a pulmonary embolism or troponin which is often elevated in myocardial infarction. Other tests are very specific, they are good at identifying people who do not have a disorder. Highly specific tests have low false positive rates (because they are very good at identifying negative cases). These low false positive rates mean that when a specific test is positive - it can be trusted. Examples of specific findings include a tissue biopsy showing colorectal cancer on a colonoscopy or the RT-QuIC assay for Creutzfeldt-Jakob disease.
The same test result can have different sensitivities and specificites depending on what it is being used for. For example, the specificity of troponin will vary depending on whether we are using it to detect myocardial ischaemia or cardiomyopathy.
Some findings in medicine are binary (yes/no) - the patient has a pulse or they do not. More commonly though, findings exist on a spectrum and this is particularly true for laboratory tests. ...
Sensitivity is a measure of how good a test is at detecting positive cases. It is the proportion of the true positives successfully detected over the total number of positives which is the sum of the true positives and the false negatives.
$$Sensitivity = \frac{TP}{TP+FN}$$
Specificity is a measure of how good a negative test is at identifying negative cases. It is the proportion of true negatives successfully detected over the total number of negatives. The total number of negatives is the sum of the true negatives and the false positives.
$$Specificity = \frac{TN}{TN+FP}$$
The positive predictive value (PPV) is the probability of a disorder given a positive test result.
$$PPV = \frac{TP}{TP+FN}$$
The negative predictive value (NPV) is the probability of no disorder given a negative test result.
$$NPV = \frac{TN}{TN+FP}$$