The terms sensitivity and specificity are used in testing hypotheses. The relevance of the two may differ depending on the type of the study. The desirable test should be able to provide results with 100 percent sensitivity and 100 percent specificity. However, in practice, this is challenging to attain. Most of the time, trade between the two is vital to building a reasonable basis for the test results’ long-term viability. As a result, the primary emphasis is on the difference between sensitivity and specificity.
Sensitivity vs Specificity
The main difference between Sensitivity and Specificity is Sensitivity is primarily concerned with calculating the likelihood of true positives. Specificity, on the other hand, is primarily concerned with calculating the likelihood of true negatives. This is the crucial distinction between sensitivity and specificity. However, in practice, 100 percent sensitivity and specificity are impossible to achieve.
Sensitivity is a metric that determines the likelihood of true positives. To put it another way, this test function is primarily concerned with finding the sample members who are truly positive about the property being tested. In a practical test, achieving 100 percent sensitivity is unattainable since it removes the mistakenly rejected portion. As a result, the goal is to achieve extremely high sensitivity; a highly sensitive test is extremely trustworthy.
Specificity is a parameter that determines the likelihood of actual negatives. The goal of this measurement is to identify the sample members who are truly negative about the property being tested. Furthermore, in medical and chemical testing, specificity is essential. In medical testing, it’s more vital to confirm that a person doesn’t have the condition than it is to discover whether or not they have.
Comparison Table Between Sensitivity and Specificity
| Parameters of Comparision | Sensitivity | Specificity |
| Definition | Sensitivity is a metric that determines the likelihood of a positive test result. | The likelihood of something being found to be false is measured by specificity. |
| 100% Value | Every person with the disease is correctly identified by a test with 100 % sensitivity. | Every person who does not have the condition is appropriately identified by a test with 100% specificity. |
| Calculation | Sensitivity = No. of true positive/ [No. of true positives + No. of false negatives] | Specificity = No. of true negative/ [ No. of true negatives + No. of false positive] |
| Probability | Probability of actual positives. | Probability of actual negatives. |
| Examples | High sensitivity test for detection of AIDS-like ELISA. | High sensitivity test for detection of AIDS-like Western blot. |
What is Sensitivity?
The frequency in which the disease positivity is identified among the patients indicates sensitivity. In fact, sensitivity confirms that the laboratory results are acceptable while testing patients for specific conditions or sickness.
A sick person’s test can result in either positive or negative results. True positive is the positive outcome, while a false negative is a negative consequence. Because of this undesirable result, an unwell person is wrongly recognized as healthy. A healthy person’s test can yield both positive and negative findings. The negative result is a true negative, whereas the positive result is a false positive in this case. As a result of this good finding, a healthy person is incorrectly categorized as ill.
The following formula is used to calculate sensitivity (in percentages):
Sensitivity = [(TP/TP+FN)] x 100
False-positive results are not taken into account when calculating a test’s sensitivity because the information from patients with 100% associated disease is used. A test with 100 percent sensitivity cannot produce false-negative results. This implies that every patient suffering from the disease will get a positive test result. All negative results in a test with 100 percent sensitivity will be true negatives. Because negative results clear out illness, this type of test is ideal for use as a screening tool. And from the other hand, positive results can include both real and false positives
What is Specificity?
The specificity or characteristic feature of a test performed is how many people without the diseases for which it has been designed are negative (in the absence of a disease). It demonstrates that someone who does not have the condition is described appropriately by the test.
The following formula is used to calculate specificity (as a percentage):
Specificity = [(TN/TN+FP)] x 100
There are no false-positive outcomes in a test with 100% specificity. In healthy people, therefore, the test is always negative. Positive outcomes are usually positive. However, the results typically involve false negatives, which have not been considered in the analysis. Because positive results are always correct, a 100% specificity test is used to confirm the diagnosis.
It is recommended to use a test with 100 percent sensitivity when there is a possibility of a problem. The patient does not have the disease if the result is negative. If the result is positive, a 100% specific test needs to be conducted. When the result is negative, the results of the prior test were false. The individual will however be affected by the disease if the result is positive.
Main Differences Between Sensitivity and Specificity
- A laboratory test’s sensitivity reveals how typically the test is positive in patients with a particular illness, whereas its specificity tells how commonly the test is negative in people without the illness.
- The following formula is used to calculate sensitivity (in percentages): [(TP/TP+FN)] x 100 = Sensitivity, Whereas the following formula is used to compute the specificity (as a percentage): [(TN/TN+FP)] x 100 = Specificity.
- A 100 percent sensitivity test reliably identifies everyone who has the ailment, whereas a 100 percent specificity test describes everyone who does not.
- Sensitivity has a higher possibility of true positives, whereas Specificity has a higher probability of true negatives.
- The ELISA test for AIDS detection has a high sensitivity, whereas the Western blot test has high specificity.
Conclusion
The statistical measures of a test’s sensitivity and specificity are referred to as sensitivity and specificity, respectively. They’re commonly used in the medical field. That is, they calculate the chances of something being positive or negative once it has been tested. In addition, both are expressed as percentages. Furthermore, reaching 100 percent sensitivity or specificity is almost impossible.
When there is a suspicion of a condition, it is ideal to use a mixture of a test with 100 percent sensitivity and a test with 100 percent specificity. The ELISA test for AIDS detection has a high sensitivity. The Western blot test has a high specificity for AIDS diagnosis.
References
- https://academic.oup.com/bjaed/article-abstract/8/6/221/406440
- https://www.bmj.com/content/327/7417/716.short