type-2 error

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type-2 error

An error made when a test fails to reject a false null hypothesis.
References in periodicals archive ?
The paper's premise is that the costs and benefits of Type-I and Type-II error avoidance vary with the disease context.
In general, for deadly diseases, Type-II error costs are larger than Type-I error costs--the value of lives saved offsets the costs of some false positives.
Conversely, the type-II error rate is defined as the complement of sensitivity (1 - sensitivity) to represent the fraction of events that take place on the "wrong" side of the threshold (development over cold SSTs in this case).
Widely used performance measures of default prediction systems are accuracy, Type-I error, Type-II error, and AUC (Verikas et al.
One explanation for that observation may be type-II error resulting from the small number of subjects in the trial.
Conversely, it is type-II error to accept the hypothesis when it is false.
Instead, it controlled for the statistical power (the complement of the type-II error rate) and uses the resulting type-I error rate, computed from the sample size and other information, for the test.
A power analysis was performed to investigate the likelihood of a type-II error. The statistical analyses were performed with SPSS software (Version 7.0 for Windows 95; SPSS, Chicago, IL) [13] and NCSS software (for the power analysis; PASS, Version 1.0 for DOS) on an IBM-compatible personal computer with a Pentium processor.
Errors in the predictions of a model occur either when the model incorrectly predicts that a bank will survive when it actually fails, a Type-I error, or when the model incorrectly predicts that a bank will fail when it actually survives, a Type-II error. A Type-I error means that the model has faltered in its early-warning capacity, so it often is considered the more serious error.
Without knowing the fate of manuscripts rejected for publication in Blank's (or any other researcher's) sample, the severity of the type-II error problem cannot be determined.
False positive and false negative are Type-I and Type-II errors respectively.
Secondly, Shrader-Frechette and McCoy recommend minimizing "type-II errors" (the probability of accepting the null hypothesis when it is in fact false) as opposed to minimizing "type-I errors" (the probability of rejecting the null hypothesis when it is in fact true).

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