type II error


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type II error

n. Statistics
The error of failing to reject a null hypothesis that is false. Also called beta error.

type II error

n
(Statistics) statistics the error of not rejecting the null hypothesis when it is false. The probability of avoiding such an error is the power of the test and is a function of the alternative hypothesis
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In sum, the ecological approach fails due to a Type II error (accepting the null hypothesis when it is false), which means believing that the available data for decisionmaking are too uncertain from a Knightian perspective to commit substantial resources or commit to irrevocable courses of action in seeking to mitigate climate change.
Low statistical power contributes to an increased likelihood of making a Type II error (Onwuegbuzie, 2004), thereby causing important findings to be either misreported or not even published.
Incorrectly rejecting the null hypothesis is called Type I error and incorrectly accepting the null hypothesis is called Type II error. For every statistical test, the probability of type I error is called [alpha] and the probability of type II error is called [beta] as it is shown in table 1.
Power of the study (1-[beta]) ([beta] is type II error) (Usually 1-[beta] is fixed at > 80%)
Type I and Type II error concerns in fMRI research: re-balancing the scale.
In the first place, even if any single Type I error is always more costly than a single Type II error, what matters is the total social cost of all errors, not the cost of any individual error or the relative costs of any two.
A Type II error takes place when the regression coefficient for level or slope change has a p value > .05 in data series with intervention effect.
Failing to reject [H.sub.o], under the constraints of committing a Type I or Type II error, is a better decision than simply accepting it, even though the two choices appear to give a similar conclusion.
Type I and Type II errors. The proportion of cases in which visual analysts detect a nonexistent effect (i.e., commit a Type I error) or miss an existing effect (i.e., a Type II error) can be used as an indicator of their performance.
It is apparent that the misclassification costs associated with Type I error (a customer with good credit is misclassified as a customer with bad credit) and Type II error (a customer with bad credit is misclassified as a customer with good credit) are significantly different.