Also called α and β error.
Type I error, α (alpha), is defined as the probability of rejecting a true null hypothesis which means detecting an effect that is not present. 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 errors include a test that shows a patient to have a disease when in fact the patient does not have the disease.(False positive cases)
Type II error, β (beta), is defined as the probability of failing to reject a false null hypothesis which means failing to detect an effect that is present. Example of type II error would be a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease. (False negative cases)
Any statistical hypothesis test has a probability of making type I and type II errors. For example, all blood tests for a disease will falsely detect the disease in some proportion of people who don't have it, and will fail to detect the disease in some proportion of people who do have it.
Tabularised relations between truth/falseness of the null hypothesis and outcomes of the test:
Type I error, α (alpha), is defined as the probability of rejecting a true null hypothesis which means detecting an effect that is not present. 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 errors include a test that shows a patient to have a disease when in fact the patient does not have the disease.(False positive cases)
Type II error, β (beta), is defined as the probability of failing to reject a false null hypothesis which means failing to detect an effect that is present. Example of type II error would be a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease. (False negative cases)
Any statistical hypothesis test has a probability of making type I and type II errors. For example, all blood tests for a disease will falsely detect the disease in some proportion of people who don't have it, and will fail to detect the disease in some proportion of people who do have it.
These error rates are traded off against each other: for any given sample set, the effort to reduce one type of error generally results in increasing the other type of error. Therefore, for a given test, the only way to reduce both error rates together is to increase the sample size.
| Table of error types | Null hypothesis (H0) is | ||
|---|---|---|---|
| Valid/True | Invalid/False | ||
| Judgment of Null Hypothesis (H0) | Reject | Type I error (False Positive) | Correct inference (True Positive) |
| Fail to reject | Correct inference (True Negative) | Type II error (False Negative) | |
Type-1 = True H0 but reject it (False Positive)
Type-2 = False H0 but accept it (False Negative)
| |||
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