Selection errors

The literature includes a number of mis-matches, the following standing as examples for the many The use of bovine liver and other animal tissues for QC in the analysis of hmnan body fluids should not be considered by analysts. The matrix and the levels of trace elements do not match the levels to be analyzed, which may lead to serious errors. An even more severe mis-use was recently reported by Schuhma-cher et al. (1996) for NIST SRM 1577a Bovine Liver, which was used for QC in the analysis of trace elements in plant materials and soil samples in the vicinity of a municipal waste incinerator. Also recently, Cheung and Wong (1997) described how the quality control for the analysis of trace elements in clams (shellfish) and sediments was performed with the same material NIST SRM 1646, Estuarine sediment. Whilst the selected SRM was appropriate for sediments, its usefulness as a QC tool for clams is difficult to prove see also Chapter 8. This inappropriate use is the more mystifying because a broad selection of suitable shellfish RMs from various producers is available. 

How critically interdependent matrix and analytical methods can be is illustrated in the example of the analysis of a soil sample. Table 7.1 shows the method dependent certified values for some common trace elements. The soil had been subjected to a multi-national, multi-laboratory comparison on a number of occasions (Houba et al. 1995) which provided extensive data. The data was subjected to a rigorous statistical program, developed for the USEPA by Kadafar (1982). This process allowed the calculation of certified values for a wide range of inorganic analytes. Uniquely, for the soil there are certified values for four very different sample preparation methods, as follows  

This method has been shown to be a step forward in the development of a universal extractant for nutrients and metals by Erp et al. (1998). 

The enormous difference in certified values between methods and between analytes illustrates well how much care is needed in matrix/method matching. Further evidence of the importance of matrix matching is provided by an interlaboratory study on trace elements in soil reported by Maier et al. (1983) and the certification of a sewage sludge described by Maaskant et al. (1998). 

In trace organic analysis there is usually an extraction or clean-up process, rather than a sample dissolution. Here not only must the matrix effect be considered, but also the recovery yield of the extraction. Frequently an external spike standard is added, but there is often no way of knowing if the recovery of the spike standard matches the analyte in question. There is considerable evidence that the U S E P A method for VOA analysis (Minnich 1993) is subject to such error, as reported by Schumacher and Ward (7997). The analyst must always consider the possibility of such an error, especially when using CRMs to control methods that are applied in routine mode. 

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The selection to minimize absolute error [Eq. (6)] calls for optimization algorithms different from those of the standard least-squares problem. Both problems have simple and extensively documented solutions. A slight advantage of the LP solution is that it does not need to be solved for the points for which the approximation error is less than the selected error threshold. In contrast, the least squares problem has to be solved with every newly acquired piece of data. The LP problem can effectively be solved with the dual simplex algorithm, which allows the solution to proceed recursively with the gradual introduction of constraints corresponding to the new data points. 

In estimating the range of ingested doses which could have resulted in a given biomarker concentration, there are three main sources of variability errors in model selection, errors in estimation of model parameters, and tme population variability (i.e., heterogeneity). The variability due to the first two sources can be reduced by the collection... 

It is essential to strictly control error rates by careful significance evaluation to save the time and effort of experiments for confirming biomarker candidates selected. Error rates can be controlled more carefully and strictly by using multiple testing adjusted p-values rather than raw p-values. 

Selection error (e.g. selects wrong display/device/setting)... 

Another form of design error can be poor material selection. A selection error may result from lack of knowledge or data about particular materials. Similar materials may have different properties that are critical. A selection error may result from lack of knowledge or from lack of field data or test data about a use environment. Selection of incompatible materials may induce or accelerate corrosion and fatigue, increase brittleness, or cause other effects that reduce the strength of the material. 

Long Range Heterogeneity (or Point Selection) Error errors of this type are nonrandom and arise from existence of spatially localized hot spots in the sampling target such errors can be identified and reduced by taking many sampling increments to form the final sample. 

In Chart Tools, Layout, select Error Bars... 

To add error bars, click on one of the points to highlight all points on the graph. In Chart Tools, Layout, select Error Bars and choose More Error Bars Options. For Error Amount, choose Custom and Specify Value. For both Positive Error Value and Negative Error Value, enter D4 D9. You just told the spreadsheet to use 95% confidence intervals for error bars. When you click OK, the graph has both jc and y error bars. Click on any jc error bar and press Delete to remove all jc error bars. 

Usually not the case since testing usually selects error-prone situations... 

During the hfe of the system there must exist a combination of values for the inputs signals that produces the following output combination both the check of the manual door open right selection (ma-nual door open right selection error) fails and the check of the manual door open left selection (manual door open left selection error) fails. ... 

PRMSOl (TP train polarity = positive ) (current platform side = left ) (driver door open rqst right = TRUE) ( ma-nual door selection error)... 

Design and material selection errors are likely to affect the whole population of the product whereas... 

Selection errors (e.g. choosing one alternative over another) selection omitted wrong selection made. 

Totally 67 errors were identified in all permit to work system tasks including 28 action error, 23 checking error, 8 retrieval error, 5 selection error and 3 information communication error. 

Flexible Unequal Error Control Codes with Selectable Error Detection and Correction Levels... 

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