# False Positive Error Rate

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A False Positive Error Rate is a binary classification performance measure that is based on the Probability that a Predictive Relation will Incorrectly Predict that a False Test Instance is a True Test Instance (i.e. make a Positive Prediction).

**AKA:**FPR, Type 1 Error Rate.**Context:****Example(s):**- The probability that a patient without disease [math]\displaystyle{ X }[/math] will receive a test result that claims they do have disease
*X*. - …

- The probability that a patient without disease [math]\displaystyle{ X }[/math] will receive a test result that claims they do have disease
**Counter-Example(s):****See:**Type 1 Error, False Discovery Rate.

## References

### 2015

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/false_positive_rate Retrieved:2015-7-19.
- In statistics, when performing multiple comparisons, the term
**false positive ratio**, also known as the false alarm ratio, usually refers to the probability of falsely rejecting the null hypothesis for a particular test.The false positive

**rate**(or "false alarm rate") usually refers to the expectancy of the false positive**ratio**.

- In statistics, when performing multiple comparisons, the term

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/false_positive_rate#Quick_Definition Retrieved:2015-7-19.
- The false positive rate is [math]\displaystyle{ \frac{FP}{FP + TN} }[/math] .
Where FP is number of false positives, and TN is number of true negatives.

- The false positive rate is [math]\displaystyle{ \frac{FP}{FP + TN} }[/math] .

### 2009

- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Type_I_and_type_II_errors#Type_I_error
- QUOTE: Type I error, also known as an "error of the first kind", an [math]\displaystyle{ α }[/math] error, or a "false positive": the error of rejecting a null hypothesis when it is actually true. Plainly speaking, it occurs when we are observing a difference when in truth there is none. An example of this would be if a test shows that a woman is pregnant when in reality she is not. Type I error can be viewed as the error of excessive credulity.

### 2008

- (Upton & Cook, 2008) ⇒ Graham Upton, and Ian Cook. (2008). “A Dictionary of Statistics, 2nd edition revised." Oxford University Press. ISBN:0199541450
- QUOTE: Alpha [math]\displaystyle{ \alpha }[/math]: The probability, in a hypothesis test, of rejecting the null hypothesis when it is, in fact, true. Usually called the significance level.

### 2003

- http://www.nature.com/nrg/journal/v4/n9/glossary/nrg1155_glossary.html
- QUOTE: SIGNIFICANCE LEVEL The proportion of false-positive test results out of all false results — that is, results that are obtained when the effect investigated is known to be absent (see also false discovery rate).