System systematic error

Systematic errors can occur anywhere in the design and implementation process or during the operational life of an SIS device. These errors put the SIS on the path to failure in spite of the design elements incorporated to achieve robust hardware and software systems. Systematic errors are minimized using work processes that address potential human errors in the SIS design and management (e.g., programming errors or hardware specification errors). 

Another claim is that the nature of the errors is different between chemical and physical measurements. It is claimed [2] that for physical systems, systematic errors predominate and that these are corrected out of the result whereas for chemical systems random errors predominate. I do not know the basis for these claims but they do not align with my own experience, e.g. the systematic error associated with recovery can easily be equal or greater than the random error. An important point about the error structures is that the ability to detect and correct for systematic errors is limited by the size of the random error, but this is true for all types of measurement. It requires 13 replicate measurements to have the sensitivity to detect an effect equal to the size of the standard deviation on 1 measurement. There is also the view that the uncertainties on physical measurements are of the order of one part per million, but again this is not true. Standards of radioactivity and neutron dose uncertainties are often in the range of 1-30%. 

Wet digestion procedures can be carried out using open or closed systems. Systematic errors can occur in open systems by contamination from digestion vessels, loss of elements by volatilisation and adsorption onto vessel surfaces. These problems led to the development of pressurised... 

In the maximum-likelihood method used here, the "true" value of each measured variable is also found in the course of parameter estimation. The differences between these "true" values and the corresponding experimentally measured values are the residuals (also called deviations). When there are many data points, the residuals can be analyzed by standard statistical methods (Draper and Smith, 1966). If, however, there are only a few data points, examination of the residuals for trends, when plotted versus other system variables, may provide valuable information. Often these plots can indicate at a glance excessive experimental error, systematic error, or "lack of fit." Data points which are obviously bad can also be readily detected. If the model is suitable and if there are no systematic errors, such a plot shows the residuals randomly distributed with zero means. This behavior is shown in Figure 3 for the ethyl-acetate-n-propanol data of Murti and Van Winkle (1958), fitted with the van Laar equation. 

It is important to verify that the simulation describes the chemical system correctly. Any given property of the system should show a normal (Gaussian) distribution around the average value. If a normal distribution is not obtained, then a systematic error in the calculation is indicated. Comparing computed values to the experimental results will indicate the reasonableness of the force field, number of solvent molecules, and other aspects of the model system. 

Raw frequency values computed at the Hartree-Fock level contain known systematic errors due to the neglect of electron correlation, resulting in overestimates of about 10%-12%. Therefore, it is usual to scale frequencies predicted at the Hartree-Fock level by an empirical factor of 0.8929. Use of this factor has been demonstrated to produce very good agreement with experiment for a wide range of systems. Our values must be expected to deviate even a bit more from experiment because of our choice of a medium-sized basis set (by around 15% in all). 

The flowsheet shown in the introduction and that used in connection with a simulation (Section 1.4) provide insights into the pervasiveness of errors at the source, random errors are experienced as an inherent feature of every measurement process. The standard deviation is commonly substituted for a more detailed description of the error distribution (see also Section 1.2), as this suffices in most cases. Systematic errors due to interference or faulty interpretation cannot be detected by statistical methods alone control experiments are necessary. One or more such primary results must usually be inserted into a more or less complex system of equations to obtain the final result (for examples, see Refs. 23, 91-94, 104, 105, 142. The question that imposes itself at this point is how reliable is the final result Two different mechanisms of action must be discussed ... 

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