the probability of making a type i error when retaining ho at the .05 level of significance is
5%
title: Type I and type II errors text: a test checking for a single condition with a definitive result of true or false. A Type II error is committed when we fail to believe a true alternative hypothesis. In terms of folk tales, an investigator may fail to see the wolf when it is present ("failing to raise an alarm"). Again, "H": no wolf. The rate of the type II error is denoted by the Greek letter β (beta) and related to the power of a test (which equals 1−β). Tabularised relations between truth/falseness of the null hypothesis and outcomes of the test: "Hypothesis:" "Adding water to toothpaste title: Type I and type II errors text: given that it is true. It is denoted by the Greek letter α (alpha) and is also called the alpha level. Often, the significance level is set to 0.05 (5%), implying that it is acceptable to have a 5% probability of incorrectly rejecting the null hypothesis. A type II error occurs when the null hypothesis is false, but erroneously fails to be rejected. It is failing to assert what is present, a miss. A type II error may be compared with a so-called false negative (where an actual 'hit' was disregarded by the test and seen as a 'miss') in title: Type I and type II errors text: been suggested as "Type III errors", though none have wide use. In practice, the difference between a false positive and false negative is usually not obvious, since all statistical hypothesis tests have 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. A test's probability of making a type I error is denoted by α. A test's probability of making a type title: Type I and type II errors text: tests always involve a trade-off between: A threshold value can be varied to make the test more restrictive or more sensitive, with the more restrictive tests increasing the risk of rejecting true positives, and the more sensitive tests increasing the risk of accepting false positives. An automated inventory control system that rejects high-quality goods of a consignment commits a type I error, while a system that accepts low-quality goods commits a type II error. The notions of false positives and false negatives have a wide currency in the realm of computers and computer applications, as follows. Security vulnerabilities are an title: Type I and type II errors text: and type II error. A type I error occurs when the null hypothesis ("H") 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). In terms of folk tales, an investigator may see the wolf when there is none ("raising a false alarm"). Where the null hypothesis, "H", is: no wolf. The type I error rate or significance level is the probability of rejecting the null hypothesis