FACTOID # 21: 15% of Army recruits from South Dakota are Native American, which is roughly the same percentage for female Army recruits in the state.
 
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Encyclopedia > Specificity (tests)

The specificity is a statistical measure of how well a binary classification test correctly identifies the negative cases, or those cases that do not meet the condition under study. For example, given a medical test that determines if a person has a certain disease, the specificity of the test to the disease is the probability that the test indicates `negative' if the person does not have the disease. Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not. ...


That is, the specificity is the proportion of true negatives of all negative cases in the population. It is a parameter of the test. Scientists recognize two different sorts of error:[1] Statistical error: the difference between a computed, estimated, or measured value and the true, specified, or theoretically correct value (see errors and residuals in statistics) that is caused by random, and inherently unpredictable fluctuations in the measurement apparatus. ...


High specificity is important when the treatment or diagnosis is harmful to the patient mentally and/or physically.

Contents

Worked example

Relationships among terms
Condition
(as determined by "Gold standard")
True False
Test
outcome
Positive True Positive False Positive
(Type I error, P-value)
Positive predictive value
Negative False Negative
(Type II error)
True Negative Negative predictive value

Sensitivity

Specificity
A worked example
the Fecal occult blood (FOB) screen test is used in 203 people to look for bowel cancer:
Patients with bowel cancer
(as confirmed on endoscopy)
True False  ?
FOB
test
Positive TP = 2 FP = 18 = TP / (TP + FP)
= 2 / (2 + 18)
= 2 / 20 ≡ 10%
Negative FN = 1 TN = 182 = TN / (TN + FN)
182 / (1 + 182)
= 182 / 183 ≡ 99.5%

= TP / (TP + FN)
= 2 / (2 + 1)
= 2 / 3 ≡ 66.67%

= TN / (FP + TN)
= 182 / (18 + 182)
= 182 / 200 ≡ 91%

Related calculations In medicine, a gold standard test is the diagnostic test that is regarded as definitive in determining whether an individual has a disease process. ... Type I errors (or α error, or false positive) and type II errors (β error, or a false negative) are two terms used to describe statistical errors. ... In statistical hypothesis testing, the p-value of a random variable T used as a test statistic is the probability that T will assume a value at least as extreme as the observed value tobserved, given that a null hypothesis being considered is true. ... The positive predictive value is the proportion of patients with positive test results who are correctly diagnosed. ... Type I errors (or α error, or false positive) and type II errors (β error, or a false negative) are two terms used to describe statistical errors. ... Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not. ... The sensitivity of a binary classification test or algorithm, such as a blood test to determine if a person has a certain disease, or an automated system to detect faulty products in a factory, is a parameter that expresses something about the tests performance. ... Fecal occult blood is a term for blood present in the feces that is not visibly apparent. ... Diagram of the stomach, colon, and rectum Colorectal cancer includes cancerous growths in the colon, rectum and appendix. ... Endoscopic images of a duodenal ulcer A flexible endoscope. ...

  • False positive rate (α) = FP / (FP + TN) = 18 / (18 + 182) = 9% = 1 - specificity
  • False negative rate (β) = FN / (TP + FN) = 1 / (2 + 1) = 33% = 1 - sensitivity
  • Power = 1 − β

The power of a statistical test is the probability that the test will reject a false null hypothesis (that it will not make a Type II error). ...

Definition

A specificity of 100% means that the test recognizes all healthy people as healthy. The maximum is trivially achieved by a test that claims everybody healthy regardless of the true condition. Therefore, the specificity alone does not tell us how well the test recognizes positive cases. We also need to know the sensitivity of the test to the class, or equivalently, the specificities to the other classes. The sensitivity of a binary classification test or algorithm, such as a blood test to determine if a person has a certain disease, or an automated system to detect faulty products in a factory, is a parameter that expresses something about the tests performance. ...


A test with a high specificity has a low Type I error rate. Type I errors (or α error, or false positive) and type II errors (β error, or a false negative) are two terms used to describe statistical errors. ...


Specificity is sometimes confused with the precision or the positive predictive value, both of which refer to the fraction of returned positives that are true positives. The distinction is critical when the classes are different sizes. A test with very high specificity can have very low precision if there are far more true negatives than true positives, and vice versa. In Wikipedia, precision has the following meanings: In engineering, science, industry and statistics, precision characterises the degree of mutual agreement among a series of individual measurements, values, or results - see accuracy and precision. ... The positive predictive value is the proportion of patients with positive test results who are correctly diagnosed. ...


See also

Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not. ... ROC curve of three epitope predictors. ... The sensitivity of a binary classification test or algorithm, such as a blood test to determine if a person has a certain disease, or an automated system to detect faulty products in a factory, is a parameter that expresses something about the tests performance. ... In statistics, a result is significant if it is unlikely to have occurred by chance, given that a presumed null hypothesis is true. ... Type I errors (or α error, or false positive) and type II errors (β error, or a false negative) are two terms used to describe statistical errors. ... The word selectivity has more meanings: Selectivity, the ability to notice/distinguish small differences. ...

External links

  • Sensitivity and Specificity Medical University of South Carolina
  • Tutorial in British Medical Journal

  Results from FactBites:
 
Prostate Cancer Tests Vary in Specificity, Sensitivity (437 words)
Some tests are more sensitive for identifying patients with cancer and others are more specific, meaning that fewer patients without cancer test false positive.
The Total PSA test, which measures nanograms of PSA per milliliter of blood, is a more sensitive test.
The Free PSA test, which measures the percentage of PSA that is not bound to proteins in the blood, is more specific.
Title 21 Code of Federal Regulations (6444 words)
Specificity and avidity tests shall be performed using test procedures approved by the Director, Center for Biologics Evaluation and Research (HFN-830), Food and Drug Administration, 8800 Rockville Pike, Bethesda, MD 20892.
Each of these tests shall be conducted and interpreted independently, and any discrepancy between the results of these two tests shall be resolved by testing with at least one additional antiserum before concluding that the antigen is present or absent.
To be satisfactory for release, each filling of Hepatitis B Surface Antigen shall be tested against the Reference Hepatitis B Antiserum Panel and shall be sufficiently potent to be able to detect the antibody in the appropriate sera of the reference panel by all test methods recommended by the manufacturer in the package insert.
  More results at FactBites »

 
 

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