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# Standard Error Sensitivity Specificity

Sensitivity (also called the true positive rate, the recall, or probability of detection[1] in some fields) measures the proportion of positives that are correctly specificity and sensitivity identified as such (e.g., the percentage of sick people who are sensitivity and specificity examples correctly identified as having the condition). Specificity (also called the true negative rate) measures the proportion of

## Sensitivity And Specificity Calculator

negatives that are correctly identified as such (e.g., the percentage of healthy people who are correctly identified as not having the condition). Sensitivity therefore quantifies the avoiding of

## Specificity Synonym

false negatives, and specificity does the same for false positives. For any test, there is usually a trade-off between the measures - for instance, in airport security since testing of passengers is for potential threats to safety, scanners may be set to trigger alarms on low-risk items like belt buckles and keys (low specificity), in order sensitivity and specificity table to increase the probability of identifying dangerous objects and minimize the risk of missing objects that do pose a threat (high sensitivity). This trade-off can be represented graphically using a receiver operating characteristic curve. A perfect predictor would be described as 100% sensitive (e.g., all sick individuals are correctly identified as sick) and 100% specific (e.g., no healthy individuals are incorrectly identified as sick); in reality any non-deterministic predictor will possess a minimum error bound known as the Bayes error rate. Contents 1 Definitions 1.1 Application to screening study 1.2 Confusion matrix 1.3 Sensitivity 1.4 Specificity 1.5 Graphical illustration 2 Medical examples 2.1 Misconceptions 2.2 Sensitivity index 3 Worked example 4 Estimation of errors in quoted sensitivity or specificity 5 Terminology in information retrieval 6 See also 7 References 8 Further reading 9 External links Definitions Terminology and derivations from a confusion matrix true positive (TP) eqv. with hit true negative (TN) eqv. with correct rejection false positive (FP) eqv. with false alarm, Type I err

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## Sensitivity Calculator

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this Article Home » Categories » Education and Communications » Subjects » Mathematics » Probability and Statistics ArticleEditDiscuss Edit ArticleHow to Calculate 95% Confidence Interval for a http://www.wikihow.com/Calculate-95%25-Confidence-Interval-for-a-Test's-Sensitivity Test's Sensitivity Community Q&A The sensitivity of a test is the percentage of individuals with a particular disease or characteristic correctly identified as positive by the test. Tests with high sensitivity are useful as screening tests to exclude the presence of a disease. Sensitivity is an intrinsic test parameter independent of disease prevalence; the confidence level of a tests sensitivity, however, sensitivity and depends on the sample size. Tests performed on small sample sizes (e.g. 20-30 samples) have wider confidence intervals, signifying greater imprecision. 95% confidence interval for a tests sensitivity is an important measure in the validation of a test for quality assurance. To determine the 95% confidence interval, follow these steps. Steps 1 Determine the tests sensitivity. This is generally given sensitivity and specificity for a specific test as part of the tests intrinsic characteristic. It is equal to the percentage of positives among all tested persons with the disease or characteristic of interest. For this example, suppose the test has a sensitivity of 95%, or 0.95. 2 Subtract the sensitivity from unity. For our example, we have 1-0.95 = 0.05. 3 Multiply the result above by the sensitivity. For our example, we have 0.05 x 0.95 = 0.0475. 4 Divide the result above by the number of positive cases. Suppose 30 positive cases were in the data set. For our example, we have 0.0475/30 = 0.001583. 5 Take the square root of the result above. In our example, it would be sqrt(0.001583) = 0.03979, or approximately 0.04 or 4%. This is the standard error of the sensitivity. 6 Multiply the standard error obtained above by 1.96. For our example, we have 0.04 x 1.96 = 0.08. (Note that 1.96 is the normal distribution value for 95% confidence interval found in statistical tables. The corresponding normal distribution value for a more stringent 99% confiden

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