Error insufficient selectivity

Limitations include difficulties in performing a correct exposure assessment, in some cases even a lack of information on exposure, insufficient sample size (i.e., small number of subjects in the study), selected group of subjects (e.g., the workforce), short length of follow-up, exposure to more than one substance, and potential errors (bias, confounding). 

The best method is the C algorithm where the derivatives are calculated analytically. In this case, the possible error in the results only arises from the fit of the equations (5) and with the number of selected conformations for the ah initio calculations that could be insufficient. The method and the derivatives are extremely accurate. In addition, the same conclusion concern the B algorithm since the first 15 decimal number of derivatives are identical to the analytical ones. Differences between these two last... 

Sampling design error—the collection of excessive or insufficient amount of data the unrepresentative selection of sampling points wrong time of sampling that may affect data relevancy the choice of improper analysis... 

Another weak point of many allelopathic research studies reported in the literature is the lack of proper statistical analysis. Probably the two most critical errors are the lack of proper controls and insufficient replication. Appropriate controls need to be included even when a minimal amount of a solvent [e.g., dimethyl sulfoxide (DMSO), acetone, etc.] is used to solublilize a compound or for extraction of the test plant material. In this situation, it is also helpful to include positive controls (known compounds at similar concentrations) for comparison to the unknown or suspected allelochemicals. As discussed earlier, it is also useful to include various species so that a range of sensitivity to the test material can be observed. For example, a bioassay using seed germination might include lettuce seed, generally a sensitive species, and other species which might vary in response to the allelochemical(s) or extract. Such selection can demonstrate plant... 

Hasty generalization The writer bases the argument on insufficient or unrepresentative evidence. Suppose, for example, you have owned two poodles and they have both attacked you. If you declare that all poodles are vicious dogs, you are making a hasty generalization. There are, of course, thousands of poodles who have not attacked anyone. Similarly, you re in error if you interview only campus athletes and then declare, University students favor a new stadium. What about the opinions of the students who aren t athletes In other words, when the generalization is drawn from a sample that is too small or select, your conclusion isn t valid. 

Outlying variances, e.g. as reflected by a Cochran test, do reveal that some sets of data suffer insufficient precision compared to the other sets obtained by other laboratories. Such sets affect the final uncertainty of the certified value but not the certified value as such. The technical discussion should address the reason why a set of data lacks precision (day to day bias ) or why in one laboratory the reproducibility figure is much lower than for the rest of the participants (repeatability figure rather than reproducibility, selection of data, not fully independent measurements etc.). Sets of data are rejected if the standard error of the mean (s/n) exceeds the standard deviation of the distribution of all the laboratory mean values. It must be stressed that BCR has accepted and even promoted alternative methods of measurements in some certification exercises, in order to back-up trueness of certified values. Finally, such methods may have shown that their precision was too poor and were not used to calculate the certified value and its uncertainty. In such cases the results are made available to the user of the CRM through the certification report. 

A set of samples not involved in the development of the SVM model has to be predicted and its error studied. If insufficient samples are available, grid search or crossvalidation should be used on the calibration samples to select a reduced suite of candidate models and, finally, the validation set predicted to assess the best one. The objective is to avoid over-fitting, which is likely to occur if special care is not devoted to this issue. ... 

A further possibility for error when using ion-selective electrodes as end point indicators arises if the selectivity is insufficient. Considering that in this case the dili ting effect on the interfering ion concentration will cause a change in EMF which has nothing to do with the titration reaction, it is easy to see its influence on a precise end point determination. 

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