Type I Hypothesis Testing Error
(Redirected from test or type I error)
Jump to navigation
Jump to search
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 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.
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.