Beta Error

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Beta Error

When testing a hypothesis, the risk of a false negative in the results. It is also called a type II error or beta risk. See also: Alpha risk.
References in periodicals archive ?
In contrast, ecological rationality and decisionmaking, when incorrect, produce a Type II error. The Type II errors of ecological rationality result when policymakers do not accept that anything approaching strict probabilities of threats can be determined, leading to high levels of uncertainty on their part, and thus a failure to act.
Power, therefore, is related to Type II error. When sample size is small, the likelihood of finding statistical significance decreases, and researchers are in danger of making a Type II error--failing to find statistical significance when it actually exists.
A statistical test with small power is resulted by high probability of type II error that leads to discovery of one non-significant effect when a significant effect does really exist.
If agencies commit a Type II error, and allow a merger to go ahead that results in anticompetitive effects, the law permits them to revisit and condemn that merger after it is consummated.
A more realistic look at the robustness and Type II error properties of the t test to departures from population normality.
Students were more sensitive to treatment effects (committing fewer Type II errors), but they also tended to commit more Type I errors.
Type I and Type II errors of four models Type I error Type II error Discriminant Analysis 31.90% 43.32% Logistic Regression 42.86% 32.22% Neural Networks 44.59% 29.25% CART 39.88% 33.01%
Furthermore, we may have accepted a false null hypothesis because of a Type II error. Factors that may have led to a Type II error in this study included the sampling technique, type of instrument, and nuisance variables.
We argue, following Foote, Gerardi, and Willen (2008), that evaluation of such proposals must balance what we call Type I error, failing to assist people who need help, against Type II error, providing costly assistance to people who do not need help.
Therefore, we studied the effect of autocorrelation on Type I and Type II error rates for each intervention point in a systematic manner.
The pharmacy should consider which is the cost of classifying a re-buying customer as a customer who is going to buy again (Type I error), and which is the cost of classifying a customer who is not going to buy again as a customer who is going to come back (Type II error).