false positive error

false positive error

Type I error Statistics An error that occurs when the statistical analysis of a trial detects a difference in outcomes between a treatment group and a control group when in fact there is no difference
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of readers with acceptable 10 (76.9) (b) performance a (%) (a) Acceptable performance: at most one low false negative error without any high false negative result nor false positive error; (b) Percentage of readers with acceptable performance in panel testing significantly increased compared with that on the baseline panel (p<0.05).
(d) The protocol has a lower probability of communication complexity and false positive error verification compared with [15].
We determined the parametric 0.1% false positive error rate confirmation cut point for both the reference drug and the biosimilar to be 71.4% and 67.5%, respectively.
False positive error is a ratio of wrongly identified duplicates.
With 238,135 requests for latent fingerprint comparisons in 2002 alone, a false positive error rate of 2 percent implies up to 4,800 false convictions or guilty pleas made in hopes of a lighter sentence each year in the U.S., 1,700 of them in felony cases.
The False Positive Error. Statistical decision theory recognizes two types of inferential error.
(274) It should also be noted that using exposed wrongful convictions to estimate the false positive error rate of a forensic technique may risk underestimating the false positive rate because it would fail to detect false positive errors in which the falsely identified individual was in fact guilty of the crime.
Trying to apply traditional design rule checking (DRC) used for CMOS to silicon photonic layouts would yield numerous false positive errors that design teams would have to spend weeks tracking down.
Trials currently require a difference in results between experimental and control patients that exceeds 1.96 standard deviations of the mean (that is, keep false positive errors to below 5 percent).
Detecting more true positives (the same as decreasing false negative errors and increasing sensitivity) always comes at a cost to increased false positive errors. ROC analyses can be used to identify the cut score that provides the best balance between types of errors.
The ratio for Blacks (3.66) shows that the system is far more likely to produce false positive errors as opposed to false negative ones, when compared to the overall population (1.31), Hispanics (1.91), or Whites (1.57).