The terms sensitivity and specificity are used in testing hypotheses. The relevance of the two may differ depending on the type of study.
Key Takeaways
- Sensitivity measures the proportion of true positive results among individuals with a specific condition.
- Specificity evaluates the proportion of true negative results among those without the condition.
- A diagnostic test with high sensitivity is useful for ruling out disease, while a test with high specificity is valuable for confirming its presence.
Sensitivity vs Specificity
In hypotheses testing, sensitivity is a parameter that measures the likelihood of a positive test result. A person high on this parameter indicates the presence of a disease or condition. Specificity is the metric to test the likelihood of a negative test result. A person without the presence of a disease or condition is identified by a high percentage on this test.
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.
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.
Comparison Table
| 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 sicknesses.
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.
The following formula is used to calculate sensitivity (in percentages):
Sensitivity = [(TP/TP+FN)] x 100
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.
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).
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 positive.
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.
Main Differences Between Sensitivity and Specificity
- 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.
- https://academic.oup.com/bjaed/article-abstract/8/6/221/406440
- https://www.bmj.com/content/327/7417/716.short
Last Updated : 20 August, 2023
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Emma Smith holds an MA degree in English from Irvine Valley College. She has been a Journalist since 2002, writing articles on the English language, Sports, and Law. Read more about me on her bio page.
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