# Type I Hypothesis Testing Error

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A Type I Hypothesis Testing Error is a hypothesis rejection decision that is a false positive prediction.

**Context:**- …

**Example(s):**- …

**Counter-Example(s):****See:**Null Hypothesis Testing, Family-Wise Error Rate, Binary Classification.

## References

### 2020

- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/type_I_and_type_II_errors Retrieved:2020-10-5.
- In statistical hypothesis testing, a
**type I error**is the rejection of a true null hypothesis (also known as a "false positive" finding or conclusion; example: "an innocent person is convicted"), while a**type II error**is the non-rejection of a false null hypothesis (also known as a "false negative" finding or conclusion; example: "a guilty person is not convicted"). Much of statistical theory revolves around the minimization of one or both of these errors, though the complete elimination of either is a statistical impossibility for non-deterministic algorithms. By selecting a low threshold (cut-off) value and modifying the alpha (p) level, the quality of the hypothesis test can be increased. The knowledge of Type I errors and Type II errors is widely used in medical science, biometrics and computer science.Intuitively, type I errors can be thought of as errors of

*commission*, and type II errors as errors of*omission*. For example, in the context of binary classification, when trying to decide whether an input image*X*is an image of a dog: an error of commission (type I) is classifying*X*as a dog when it isn't, whereas an error of omission (type II) is classifying*X*as not a dog when it is.

- In statistical hypothesis testing, a

### 2009

- http://www.introductorystatistics.com/escout/main/Glossary.htm
- QUOTE: type I (hypothesis test) error: The error of incorrectly rejecting a null hypothesis when it is true.