Difference between revisions of "Specificity And Sensitivity"
From Sean_Carver
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* A false positive result means the truth is ''negative.'' So the test might have been '''a true negative''' instead of being '''a false positive''', those events are disjoint, and if the truth is negative, the result can't be anything else. | * A false positive result means the truth is ''negative.'' So the test might have been '''a true negative''' instead of being '''a false positive''', those events are disjoint, and if the truth is negative, the result can't be anything else. | ||
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| + | * If the truth is negative, PROBABILITY(False Positive) + PROBABILITY(True Negative) = 1 | ||
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| + | * Knowing one probability you can find the other | ||
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| + | * The '''Specificity''' is the Probability of a True Negative, assuming the truth is Negative. | ||
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| + | Similarly... | ||
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| + | * The '''Sensitivity''' is the Probability of a True Positive, assuming the truth is Positive. | ||
Revision as of 19:21, 1 August 2016
Specificity and Sensitivity
Medical journals often report the Specificity and Sensitivity of tests for things like HIV or the Zika virus. These measures describe the rates of Type I and Type II errors.
- Specificity, or sPecificity, concerns the rate of false Positives.
- Sensitivity or seNsitivity concerns the rate of false Negatives.
This is where it gets confusing...
- A false positive result means the truth is negative. So the test might have been a true negative instead of being a false positive, those events are disjoint, and if the truth is negative, the result can't be anything else.
- If the truth is negative, PROBABILITY(False Positive) + PROBABILITY(True Negative) = 1
- Knowing one probability you can find the other
- The Specificity is the Probability of a True Negative, assuming the truth is Negative.
Similarly...
- The Sensitivity is the Probability of a True Positive, assuming the truth is Positive.
