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How To Calculate Specificity And Sensitivity
Use our calculator to determine diagnostic test accuracy metrics including sensitivity, specificity, PPV, and NPV
Test Results For Disease-Positive
Test Results For Disease-Negative
Diagnostic Test Results
Sensitivity
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True Positive Rate
Specificity
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True Negative Rate
Positive Predictive Value
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Precision
Negative Predictive Value
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Negative Precision
Understanding The Metrics
Sensitivity: The ability of a test to correctly identify those with the disease (true positive rate).
Specificity: The ability of a test to correctly identify those without the disease (true negative rate).
Positive Predictive Value (PPV): The probability that subjects with a positive test truly have the disease.
Negative Predictive Value (NPV): The probability that subjects with a negative test truly don't have the disease.
How To Calculate Specificity And Sensitivity
Understanding how to calculate specificity and sensitivity is essential for evaluating diagnostic tests in medicine, research, and data science. These metrics help determine how well a test can identify true positives and true negatives.
What Is Sensitivity?
Sensitivity (also called the true positive rate) measures the proportion of actual positives that are correctly identified by the test. A test with high sensitivity is good at detecting people who have the condition.
What Is Specificity?
Specificity (also called the true negative rate) measures the proportion of actual negatives that are correctly identified by the test. A test with high specificity is good at correctly ruling out people who don't have the condition.
Formulas For Calculation
- Sensitivity = True Positives / (True Positives + False Negatives)
- Specificity = True Negatives / (True Negatives + False Positives)
- Positive Predictive Value (PPV) = True Positives / (True Positives + False Positives)
- Negative Predictive Value (NPV) = True Negatives / (True Negatives + False Negatives)
When To Use These Metrics
Sensitivity and specificity calculations are crucial in medical testing, machine learning model evaluation, quality control processes, and any scenario where you need to assess the accuracy of a binary classification system.
Interpreting The Results
Perfect tests would have 100% sensitivity and 100% specificity, but in reality, there's often a trade-off between these two metrics. The ideal balance depends on the consequences of false positives versus false negatives in your specific application.
