Sensitivity and Specificity

Sensitivity and specificity are study measurements used in research analysis. They are used to measure the data performance of given values in terms of its positive or negative predictive values. Likewise, these measurements are used most commonly in the field of medical research.

Sensitivity refers to the portion of the study population whose actual positives are correctly identified. Specificity refers to the measured proportion of the study population whose negatives are correctly identified.

For example, imagine that a medical research is ongoing whose objective is to determine if a population of patients are diabetic or not. The study outcome can be either positive (diabetic) or negative (healthy):
True positive - Diabetic people correctly diagnosed as diabetic
False positive - Healthy people wrongly identified as diabetic
True negative - Healthy people correctly identified as healthy
False negative - Diabetic people wrongly identified as healthy

To calculate for the sensitivity and specificity of the given study just use the following equations:
Sensitivity = number of True Positives / (number of True Positives + number of False Negatives)
Specificity = number of True Negatives / (number of True Negatives + number of False Positives)

In interpreting the study results, the higher the sensitivity the less likely the disease is present (rule out the disease) and the higher the specificity the more likely the disease is present (rule in the disease).