Difference between revisions of "Specificity And Sensitivity"
From Sean_Carver
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* Knowing one probability you can find the other | * Knowing one probability you can find the other | ||
| − | * The '''Specificity''' is the Probability of a True Negative, assuming the truth is Negative. | + | * The '''Specificity''' is the '''Probability of a True Negative, assuming the truth is Negative'''. |
* Knowing the s'''P'''ecificity, and applying the formula, you can figure out the probability of a False '''P'''ositive. | * Knowing the s'''P'''ecificity, and applying the formula, you can figure out the probability of a False '''P'''ositive. | ||
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* If the truth is positive, the test could still be positive or negative, so 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 19:36, 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.
