Type I error

Type I error

A Type I error (or false positive) is the incorrect rejection of a true null hypothesis. Usually a type I error leads one to conclude that a supposed effect or relationship exists when in fact it doesn't. Examples of type I error include a test that shows a patient to have a disease when in fact the patient does not have the disease, a fire alarm going on indicating a fire when in fact there is no fire, or an experiment indicating that a medical treatment should cure a disease when in fact it does not.

Definition

A type I error occurs when the null hypothesis (H0) is true, but is rejected. It is asserting something that is absent, a false hit. A type I error may be likened to a so-called false positive (a result that indicates that a given condition is present when it actually is not present).

Statistical test theory

The definition for a type I error is standard in statistical test theory defining an error as the incorrect rejection of a true null hypothesis. But the increase of false positives in large scale multiple testing is a well-known problem. The rate of the type I error is the level of the test and is usually denoted by the Greek letter α (alpha).

Related terms

In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (a "false positive"), while a type II error is failing to reject a false null hypothesis (a "false negative"). More simply stated, a type I error is to falsely infer the existence of something that is not there, while a type II error is to falsely infer the absence of something that is.

See also

• Type II error
• False positives and false negatives
• Statistical hypothesis testing
• Null hypothesis

References

Type I error Resources
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