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 categorized any other way. | * 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 categorized any other way. | ||
| − | * If the truth is negative, PROBABILITY(False Positive) + PROBABILITY(True Negative) = 1 | + | * If the truth is negative, the test could still be positive or negative, so PROBABILITY(False Positive) + PROBABILITY(True Negative) = 1 |
* Knowing one probability you can find the other | * Knowing one probability you can find the other | ||
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Similarly... | Similarly... | ||
| − | * If the truth is positive, PROBABILITY(False Negative) + PROBABILITY(True Positive) = 1. | + | * If the truth is positive, the test could still be positive or negative, so PROBABILITY(False Negative) + PROBABILITY(True Positive) = 1. |
* The '''Sensitivity''' is the Probability of a True Positive, assuming the truth is Positive. | * The '''Sensitivity''' is the Probability of a True Positive, assuming the truth is Positive. | ||
* Knowing the se'''N'''sitivity, and applying the last formula, you can figure out the probability of a False '''N'''egative. | * Knowing the se'''N'''sitivity, and applying the last formula, you can figure out the probability of a False '''N'''egative. | ||
Revision as of 19:35, 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: False Positive Results are related to True Negative results and False Negative results are related to True Positive results. Huh?
- 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 categorized any other way.
- If the truth is negative, the test could still be positive or negative, so 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.
- Knowing the sPecificity, and applying the formula, you can figure out the probability of a False Positive.
Similarly...
- If the truth is positive, the test could still be positive or negative, so PROBABILITY(False Negative) + PROBABILITY(True Positive) = 1.
- The Sensitivity is the Probability of a True Positive, assuming the truth is Positive.
- Knowing the seNsitivity, and applying the last formula, you can figure out the probability of a False Negative.
