False negative decision

False positive and false negative decisions result from unreliabilities that can be attributed to uncertainties of quantitative tests. According to Fig. 4.9 the belonging of test values to the distributions p(yLSp) or p(yscR)> respectively, may be affected by the risks of error a and (see Sect. 4.3.1) which corresponds to false positive (a) and false negative (/3) test results. 

If an analytical test results in a lower value x, < x0, then the customer may reject the product as to be defective. Due to the variation in the results of analyses and their evaluation by means of statistical tests, however, a product of good quality may be rejected or a defective product may be approved according to the facts shown in Table 4.2 (see Sect. 4.3.1). Therefore, manufacturer and customer have to agree upon statistical limits (critical values) which minimize false-negative decisions (errors of the first kind which characterize the manufacturer risk) and false-positive decisions (errors of the second kind which represent the customer risk) as well as test expenditure. In principle, analytical precision and statistical security can be increased almost to an unlimited extent but this would be reflected by high costs for both manufacturers and customers. 

Type II error (beta error) An incorrect decision resulting from failing to reject the null hypothesis when the alternative hypothesis is true. A false negative decision. 

Baseline condition is true (H0 P > Ca) Correct decision The probability of making a correct decision (1—a) False acceptance decision error False negative decision error Type II decision error Probability ji Risk, error rate 100 x ft... 

If a sample actually contained an amount equal to the MDL, then 50% of the time a result would be obtained which was below MDL. Failure to report that result can induce a "false negative" decision. 

Decision taken. null hypothsis Ho our product is the same have R D come up with new ideas false negative loss of a good marketing argument hopefully the customer will appreciate the difference in quality Risk is hard to estimate... 

Because of the Importance of their decisions and the need for statistical Justification of their results, monitoring statisticians and chemometrlclans are being asked by their customers to use hypothesis testing with Its attention to false positives and false negatives. 

Indoor Level Correct Decision False Negative (Conclude Clean ... 

Consider with stakeholders the uncertainties in risks, costs and benehts, and the consequences of false positives and false negatives when establishing decision rules. 

The decision about which error is more important is not a scientific question that can be resolved through technical analysis. It is a value choice. Of course, better scientific knowledge reduces the probability of making incorrect inferences abouf healfh effects. But even in situations of high certainty, the choice between false-positive and false-negative errors remains. And people invariably weigh the trade-offs differenfly. 

Finally we hope to see that more validation studies are conducted to compare any new search method with the reference exhaustive search (of course on a smaller validation virtual space of 104-106). Only through this type of rigorous validation studies, one can truly probe the rates of false positives and false negatives as well as the fold increase in search speed. This in turn allows end users to make informed decisions on which search method will be a best match for their specific tasks. 

Based on the sample data, we may accept the null hypotheses when in fact it is false, and make a false acceptance decision error or a false negative error. In... 

This phase classifies chemicals passing from the previous phase into active and inactive categories. Three structural alerts (Section IV.B), seven pharmacophore queries (Section IV.C), and the Decision Tree classification model (Section IV.D) were used in parallel to discriminate active from inactive chemicals. To ensure the lowest false negative rate in this phase, a chemical predicted to be active by any of these 11 models is subsequently evaluated in Phase III, whereas only those predicted to inactive by all these models are eliminated for further evaluation. Since structural alert, pharamacophore and Decision Tree methods incorporate and weight differently the various structural features that endow a chemical with the ability to bind the ER the combined outputs derived... 

In both screening and predictive modeling, the relative cost of false negatives versus false positives is an important component of decision making. From a business standpoint, false negatives represent lost opportunities and false positives represent wasted efforts chasing down red herrings. The resources wasted... 

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