Type I error
Type I Error
Type I error (pronounced /taɪp wʌn ˈɛrər/), also known as a false positive, is a type of statistical error that occurs when a null hypothesis is rejected even though it is true. The term "Type I error" originates from the field of Statistical hypothesis testing, where it is used to denote the incorrect rejection of a true null hypothesis.
Etymology
The term "Type I error" was coined by Jerzy Neyman and Egon Pearson in their development of Neyman–Pearson lemma, a fundamental concept in hypothesis testing. The term is used to differentiate it from a Type II error, where a false null hypothesis is not rejected.
Definition
In Statistics, a Type I error occurs when a statistical test incorrectly indicates a specific condition or effect. This typically happens when the null hypothesis (H0) is true, but the test concludes it is false. This can lead to incorrect conclusions and potentially misleading results.
Related Terms
- Type II error: A statistical error where a false null hypothesis is not rejected.
- Null hypothesis: A general statement or default position that there is no relationship between two measured phenomena.
- Statistical hypothesis testing: A method in statistics that uses sample data to evaluate two mutually exclusive statements about a population.
- P-value: The probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct.
- Significance level: A limit set on the probability of incorrectly rejecting the null hypothesis.
See Also
External links
- Medical encyclopedia article on Type I error
- Wikipedia's article - Type I error
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