false negative error

false negative error

Type II error Statistics An error which occurs when the statistical analysis of a trial detects no difference in outcomes between a treatment group and a control group when in fact a true difference exists
McGraw-Hill Concise Dictionary of Modern Medicine. © 2002 by The McGraw-Hill Companies, Inc.
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Thus, for listeners, who are native Russian speakers, a higher score of detection of utterance in noise was shown for utterances in German, pronounced by native German speakers, than for utterances in Russian, pronounced by native Russian speakers (see Figure 3: a statistically significantly higher (at the level of 95%) value of utterance miss (false negative error) for German (depending on the characteristics of the speaker) is marked with a frame), which demonstrates the effect of the language (and its acoustic characteristics) of the utterance on detection and recognition of the utterance in noise.
The False Negative Error. The false negative, or Type II, error is committed by concluding that a condition does not exist or that a proposition is not true when in fact the condition does exist or the proposition is true.
where we weight the false negative error as twice as bad as the lack of specificity.
For biometric recognition, added liveness and spoofing mechanisms are subject to false positive and false negative errors. False positive errors wrongly categorize bona fide presentations as attack presentations, potentially flagging or inconveniencing legitimate users.
(9,10) Errors can also be classified into false positive and false negative errors. (9,10) False negative errors occur five times more commonly than false positive errors.
For example, where the cost of failing to detect a true positive carries very negative consequences, a higher false-positive rate may be tolerated in favor of ensuring fewer false negative errors. On the other hand, it is feasible that in some cases the cost associated with a false-positive error may be less tolerable than those associated with making a false-negative error.
For example, suppose that for 1000 majority group families, the reporter makes diagnostic misclassifications 10% of the time, with symmetrical error rates for both false positive and false negative errors. This would yield the pattern of results presented in Table 9.
There would be 5 appeals of false negative errors that would
False negative errors occur when the underlying unobserved truth is different from zero effect.
All false negative errors (missed SNPs) of the 1248 centered cases are listed according to background sequence, product size, and SNP type in Table 1.