False negative example

Two additional false-negative examples are described below. The first type, the prozone/high-dose hook effect, may occur in lateral-flow assay and one-step sandwich ELISA, which happens when the target protein is present in a sample at a very high concentration. Both capture and labeled antibodies are fully occupied by existing target protein and fail to form the sandwich complex (i.e., labeled-antibody/target... 

The major drawback of this method is that Bonferroni s Inequality is a conservative correction, especially if some of the hypotheses being tested are not independent. When many SNPs in the same gene are evaluated, for example, and are in LD with each other, the Boneferroni correction would not be appropriate, resulting in the possibility of false negatives or failure to detect a true association. A better approach would be to test the true level of significance directly through simulations. 

For tests designed to detect the presence or absence of an analyte, the threshold concentration that can be detected can be determined from replicate measurements over a range of concentrations. These data can be used to establish at what concentration a cut-off point can be drawn between reliable detection and non-detection. At each concentration level, it may be necessary to measure approximately ten replicates. The cut-off point depends on the number of false negative results that can be tolerated. It can be seen from Table 4.7 that for the given example the positive identification of the analyte is not reliable below 100 xg g-1. 

Predictive validity is the ability of a model to predict the effect that pharmacological or other manipulations will have on the condition being modeled. This criterion can present a real difficulty, in that drug development is often dictated by animal models. For example, if a given model only detects a subset of effective compounds (i.e. those belonging to a specific chemical class), then useful candidates will be discarded long before clinical trials, and the flaw in the model s predictive validity will not be discovered. Thus, the possibility that a model will yield false negatives cannot be ruled out. 

These are well-known classification parameters true positive rate (p ), false positive rate (pnx), true negative rate (qnx), and false negative rate (qK). They can be easily obtained from the previous computations where we calculated the number of taxon and nontaxon members in each interval. For example, to calculate the true positive rate, we sum the number of taxon members in intervals above the hitmax, plus half of taxon members in the hitmax interval and divide this by the total number of taxon members in the sample. To calculate the false negative rate, we sum number of taxon members in intervals below the hitmax, plus half of the taxon members in the hitmax interval and divide this by the total number of taxon members in the sample. 

For example, if the coefficient of variation is 2 and the level is twice the other then seventy-three samples are required to achieve a false negative rate of 5 percent. To achieve a false negative rate of 1 percent, one hundred and twelve samples would be required. As the acceptable difference between levels increase, the required number of sample required decreases. When one level is one hundred times the other and the coefficient of variation is 1, only three samples are required to achieve a false negative rate of 1 percent. 

So far we have assumed that both air levels would have to be determined and therefore that two sets of J samples would have to be collected. In some cases one level may already be available from other records and can be used as a standard of comparison. Table VI shows for this special case how the probability of a false negative depends on the number of samples collected. For small differences between the measured level and the standard a small sample size has an unacceptable high false negative rate. For example, if the mean is five times the standard level and the coefficient of variation is... 

The fact that there are false negatives can lead to extremely severe disasters in safety critical applications. For example, the case below explained by an anonymous user ... 

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