Analysis frequently occurring error

If only indeterminate errors were involved, the most frequently occurring result would be the true result, that is, the result at the maximum of the curve would be the true answer. In practice it is not possible to make an infinite number of analyses of a single sample. At best, only a few analyses can be carried out, and frequently only one analysis of a particular sample is possible. We can, however, use our knowledge of statistics to determine how reliable these results are. The basis of statistical calculations... 

This type of outlier generally arises when either the experimenter makes a mistake in creating the calibration mixtures or there was an error in the analysis of the sample from the primary calibration technique used to generate the calibration concentration values. Another possibility that frequently occurs is a transcription error. The analyst simply types in the wrong concentration value when building the computerized training set. 

In view of the selective character of many colorimetric reactions, it is important to control the operational procedure so that the colour is specific for the component being determined. This may be achieved by isolating the substance by the ordinary methods of inorganic analysis double precipitation is frequently necessary to avoid errors due to occlusion and co-precipitation. Such methods of chemical separation may be tedious and lengthy and if minute quantities are under consideration, appreciable loss may occur owing to solubility, supersaturation, and peptisation effects. Use may be made of any of the following processes in order to render colour reactions specific and/or to separate the individual substances. 

When action level excursions or frequent alert excursions are identified, a corrective action program, resolution deadline, and preventive plan shall be implemented. Risk analysis shall be performed to determine the probability of one or more causes of errors occurring, as well as to identify the potential consequences of excursions. (See attachment nos. 1700.80(1), 1700.80(J), and 1700.80(K), and 1700.80(L) for excursion of air, surface, personnel, and visual inspection report during the visit to plant by the microbiologist responsible.)... 

Autocorrelated variables and autocorrelated errors, which occur frequently in time series analysis, violate the general regression assumption of uncorrelated errors. 

The random error may be caused by the incorrect labeling of a specimen with reference to the patient s identity the accidental switching of specimens within the laboratory clerical error at any stage of the process of reception, analysis, or reporting. It is difficult to quantitate such errors, but all reasonable attempts at assessment indicate that such errors occur much more frequently than laboratory heads admit. Figures of 5% have been quoted and are too high. Figures of 1% are much more realistic but may be too low. 

Concern has been expressed about the chemical integrity of samples collected less frequently than the duration of a single storm. There is reason for some scientific inquiry on this matter, but the available data suggest that any chemical changes in a sample will occur in a relatively brief period after the precipitation has ended ( ). However, event samples may not be any more stable than weekly samples if the delay between collection of the sample and its analysis is of the order of one or more days. Consequently, until real-time chemical analysis can be performed in the field, all currently available data contain largely unknown errors from chemical changes that occur between the end of an event and the analysis. 

Most HR A methods use PSFs to support the analysis of the contextual conditions under which human failure may occur. In the qualitative HRA, PSFs help analysts to systematically address all factors believed to be important to human performance. In the quantitative HRA, PFSs are frequently used to modify a generic error probability to capture the effect of the context in which the task of interest is carried out. 

A problem encountered in CRIOP analyses is that the operators participating in the analysis lack the necessary imagination to envisage rare events. They base their experiences on events occurring with a frequency of up to 0.1 per year. Failures of technical barriers (e.g. a deluge skid on an offshore platform) or severe operator errors are usually much less frequent. 

One frequent mistake in a laser diffraction measurement arises not from the choice of sample module or the dispersion procedure but from the use of an inappropriate optical model in data analysis. This often produces an incorrect size distribution, even though the measurement was performed carefully and the instrument is functioning correctly. T5q)ical errors that may occur when an inappropriate optical model is chosen (by using the Fraunhofer model for particles smaller than 2 m, for example) are 1) multiple artificial peaks, 2) excessive peak broadening, and, 3) incorrect reporting of the mean size. 

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