type II error

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Related to type II error: Hypothesis testing, Null hypothesis, Statistical power


a defect or mistake in structure or function.
inborn error of metabolism a genetically determined biochemical disorder in which a specific enzyme defect produces a metabolic block that may have pathologic consequences at birth, as in phenylketonuria, or in later life.
measurement error the difference between what exists in reality and what is measured by a measurement method.
Type I error the rejection of a null hypothesis that is true.
Type II error acceptance of a null hypothesis that is false.

er·ror of the sec·ond kind

in a Neyman-Pearson test of a statistical hypothesis, the probability of accepting the null hypothesis when it is false; the complement of the power of the test.

type II error

in a test of a statistical hypothesis, the probability of accepting the null hypothesis when it is false and should be rejected. Also called beta error, error of the second kind.

type II error

β error, false-negative error Statistics Acceptance of the null hypothesis when it is false or incorrect, or the error of falsely stating that two proportions are not significantly different when they actually are. See Null hypothesis Cf Type 1 error.
References in periodicals archive ?
Whether these trends are true changes in disease incidence or artifacts of changing reporting or diagnostic practices, anything that causes disease counts in the baseline period to be systematically higher than current disease counts increases type II errors, and anything that causes baseline disease counts to be systematically lower than current disease counts increases type I errors.
Prediction accuracy of single classifiers Model Accuracy Type I error Type II error MLP 82.
Despite their fundamental importance, the minimum difference of interest and the type II error rates are rarely referred to when authors report their 'no statistically significant difference' results.
Heimann (1993) shows, however that, in a three-state world in which one is concerned about committing either a Type I or Type II error (and where the third state is the correct decision), serial systems are superior because they reduce the likelihood of Type I errors--at is, rejecting the null hypothesis when it is actually correct (acting when no action is necessary or desirable).
Type I and Type II error rate trade-offs must be examined for the model in Table 6 and the holdout sample (2000-2005) to determine prediction ability.
Previous studies do not take into account the likelihood that costs of Type I errors (misclassifying failures as non-failures) are higher that the costs of Type II errors (misclassifying non-failures as failures).
In fact, by increasing the risk of type I error, the risk of type II error decreases (Foster, 2001) (Fig.
Type I and Type II errors of four models Type I error Type II error Discriminant Analysis 31.
Factors that may have led to a Type II error in this study included the sampling technique, type of instrument, and nuisance variables.
The repercussions of making a Type I error or a Type II error differ greatly, however, for FDA drug reviewers.
Type II error is the misclassification of a healthy firm as bankrupt.
These figures translate into an overall classification accuracy of 88 percent with 12 percent of Type I error and 7 percent of Type II error, one year prior to bankruptcy.