Manipulation error

Standard Error Comparable with Manipulative Errors... 

Two manipulative errors that occur generally in laboratory x-ray emission spectrographs qualify for special attention. The first is the error in setting the goniometer—the reset error. The second is that traceable to the placing of samples in the spectrograph, either to the repeated placement of a single sample, or to the replacement of one... 

For work of the highest precision, it is highly advisable to carry through an analysis of variance together with suitable tests of significance, not only to establish what the precision is, but also to uncover individual sources of error so that they can be made less serious. How this is done for instrumental and manipulative errors has been demonstrated in this chapter. 

Manganese, determination by x-ray emission spectrography, 328 in domestic ores, 200, 202, 203 trace analysis by x-ray emission spectrography, 228, 229, 231, 232 Manipulative errors, standard counting error comparable with, 285-287 Mass absorption coefficient, additivity, 15... 

There are several sources of irreproducibility in kinetics experimentation, but two of the most common are individual error and unsuspected contamination of the materials or reaction vessel used in the experiments. An individual may use the wrong reagent, record an instrument reading improperly, make a manipulative error in the use of the apparatus, or plot a point incorrectly on a graph. Any of these mistakes can lead to an erroneous rate constant. The probability of an individual s repeating the same error in two successive independent experiments is small. Consequently, every effort should be made to make sure that the runs are truly independent, by starting with fresh samples, weighing these out individually, etc. Since trace impurity effects also have a tendency to be time-variable, it is wise to check for reproducibility, not only between runs over short time spans, but also between runs performed weeks or months apart. 

In general, ELISA tests have been implemented in meso-scale instrumentation based on the microtitre plate format, which has become a standard, very widely spread configuration. The analyses are usually performed with a protocol that enables the thermodynamic equilibrium of the immunoreaction to be reached at each step of the assay. In this manner, the capture efficiency is optimised and the obtained results are often very satisfactory in terms of sensitivity and reproducibility. In order to further increase the performances and throughput of these tests, fully automatic robotised stations have been developed, thereby reducing manipulation errors such as dilution or pipetting imprecision, for instance. 

The most important advantage of such an IDMS procedure is that it compensates in an ideal way both for losses during sample workup and for variations in mass spectrometric response. As demonstrated by Claeys et al. (1977), the use of a stable isotope-labeled internal standard, as opposed to the use of homologs, produces the lowest variance factors due to instrumental stability and sample manipulating errors. Moreover, IDMS can be used both with and without prior chromatographic separation, as for instance in... 

Solving gas law problems using these formulas is a straightforward process of algebraic manipulation. Errors commonly arise from using improper units,... 

Using standard solutions for quantifying concentrations in an unknown sample may give rise to measurement errors due to the influence of food matrix remnants in the injection solution. Ion suppression or enhancement is a typical matrix effect seen in mass spectrometry. Matrix-matched standards are generally used in order to avoid such possible matrix interferences. Standard addition is a valid alternative for dealing with matrix effects. On the other hand, standard addition or matrix based calibration curves require more manipulation, which is time- and money-consuming, whereas a greater risk of manipulation errors ensues. 

The Auto-LiPA procedure allows the automated processing of 30 strips in one run. After denaturation of the sample, the system proceeds automatically with the full procedure until final coloration of the strips. Manipulation errors are therefore excluded. One total run takes approx 2.5 h. The procedure allows highly standardized and reliable testing. 

Solving ideal gas law problems is a straightforward process of algebraic manipulation. Errors commonly arise from using improper units, particularly for the ideal gas constant R. An absolute temperature scale (see Skill 3.1 d) must be used—never °C— and is usually reported using the Kelvin scale, but volume and pressure units often vary from problem to problem. 

The observed yields in entries 2 and 3 are still a result of the product instability to reagent excesses and work-up conditions. Entries 5 and 6 indicate that when the enolate excess falls below a certain value, the reaction is very slow and incomplete conversion is observed. Entry 8 shows the remarkable effect of water. Here the (+)-DlP-Cl was exposed to atmospheric moisture for one to two seconds simply by removing the stopper of the weighing flask. No aldol product was formed and a 1/1 mixture of 8 and its isomer 54 was observed. The conditions of entry 4 are apparently optimal. However, the reaction is very intolerant of manipulative errors and very narrow limits between stow reaction and product stability are apparent. These conditions brought a benefit. The crude product was found to be stable indefinitely to the reagents and the work-up system. Stability tests of 64a showed no loss in yield even when the mixture was kept at 40 °C. 

Selenium is probably the furnace determination which most demands Zeeman correction STPF technology. Other methods are slow and prone to manipulative errors at the low concentrations that are typically of interest in biological materials. Nevertheless, the volatility of many Se compounds, especially organoselenium compounds, produces troubles. Both Fe and P cause severe overcorrection errors when Se is determined with continuum correction, making Zeeman correction mandatory for Se in biological materials. There are many papers in the literature that have not used Zeeman correction for Se but they rely on delicate timing of the thermal program so that Se is not volatilized at the same time as the interferent. The paper of Verlinden et al. (1981) on the MS determination of Se should be consulted. 

A 66-year-old man with severe dermatomyosi-tis received a high dose of sucrose-stabilized intravenous immunoglobuhn [57 ]. Because of a manipulation error, the first bottle of the... 

This is due to the wide range of elevated risks associated with pharmacy preparation, including calculation and manipulation errors, formulation failures leading to overdose or underdose, possible toxicity from raw materials and microbiological contamination. The relative lack of... 

Manipulation Error. Errors of manipulation occur when an operator draws the proper conclusion from the data but errs in his actions. There have been cases where the right action was taken, but on the wrong control loop. The probability of manipulation error is reduced by good graphic design. The suggestions listed above under "Wrong Indication" also apply here. 

Manipulation Error—Undertaking the wrong action from received data possibly from poor graphic design... 

Note In all cases a greater precision is possible with oxygen determinations per se (see Part 1.3) than is implied above, but other manipulative errors are involved in the present method. 

Sources of Indeterminate Error Indeterminate errors can be traced to several sources, including the collection of samples, the manipulation of samples during the analysis, and the making of measurements. 

Additional trial and error manipulation of the data might yield agreement over a somewhat wider range of conditions with slightly modified parameters. 

Some of the inherent advantages of the feedback control strategy are as follows regardless of the source or nature of the disturbance, the manipulated variable(s) adjusts to correct for the deviation from the setpoint when the deviation is detected the proper values of the manipulated variables are continually sought to balance the system by a trial-and-error approach no mathematical model of the process is required and the most often used feedback control algorithm (some form of proportional—integral—derivative control) is both robust and versatile. 

Feedforward Control If the process exhibits slow dynamic response and disturbances are frequent, then the apphcation of feedforward control may be advantageous. Feedforward (FF) control differs from feedback (FB) control in that the primary disturbance or load (L) is measured via a sensor and the manipulated variable (m) is adjusted so that deviations in the controlled variable from the set point are minimized or eliminated (see Fig. 8-29). By taking control action based on measured disturbances rather than controlled variable error, the controller can reject disturbances before they affec t the controlled variable c. In order to determine the appropriate settings for the manipulated variable, one must develop mathematical models that relate ... 

Step 9 Apply steps to inlet and bypass valves. Now that the new inlet and bypass valve positions are determined, the outputs to the valves can be changed. Before doing this, however, due to flexibility in the control system, it is still possible to manipulate the step on the valve. For this purpose, the control system provides scaling factors between the actual step and the calculated step. These scaling factors can help compensate for calculation errors and/or process dynamics. This is formulated as ... 

Feedback that confirms I am doing the right thing is important for error recovery as well as for error prevention. It is important to display the actual position of what the operator is manipulating, as well as the state of the variable he/she is worried about. 

Due to its nature, random error cannot be eliminated by calibration. Hence, the only way to deal with it is to assess its probable value and present this measurement inaccuracy with the measurement result. This requires a basic statistical manipulation of the normal distribution, as the random error is normally close to the normal distribution. Figure 12.10 shows a frequency histogram of a repeated measurement and the normal distribution f(x) based on the sample mean and variance. The total area under the curve represents the probability of all possible measured results and thus has the value of unity. 

Skill-based Errors manual variability strong but wrong action sequences Train for physical and manipulative skills (repeated practice and feedback) Checklists setting out starting and finishing activities and checks Layout and labeling of controls and process lines Distinguish tetween plant areas with similar appearance but different functions Provide feedback... 

It is important, however, not to take this analogy too far. In general, the performance of a piece of hardware, such as a valve, will be much more predictable as a function of its operating conditions than will human performance as a function of the PIFs in a situation. This is partly because human performance is dependent on a considerably larger number of parameters than hardware, and only a subset of these will be accessible to an analyst. In some ways the job of the human reliability specialist can be seen as identifying which PIFs are the major determinants of human reliability in the situation of interest, and which can be manipulated in the most cost-effective manner to minimize error. 

A parameter such as a rate constant is usually obtained as a consequence of various arithmetic manipulations, and in order to estimate the uncertainly (error) in the parameter we must know how this error is related to the uncertainties in the quantities that contribute to the parameter. For example, Eq. (2-33) for a pseudo-first-order reaction defines k, which can be determined by a semilogarithmic plot according to Eq. (2-6). By a method to be described later in this section the uncertainty in itobs (expressed as its variance associated with cb. Thus, we need to know how the errors in fcobs and cb are propagated into the rate constant k. 

When s significantly exceeds sc, other errors are present, and these may be in the equipment, in manipulation, or in the sample (either unknown or standard). 

Harris, W. E., Sampling, Manipulative, Observational, and Evaluative Errors, International Laboratory, Jan-Feb 1978, 53-62. 

Clearly the improved understanding of colloidal behaviour within living systems that we are developing offers the eventual prospect of our being able to manipulate such systems. The control of microarchitecture in both living and synthetic systems has many potential applications. The most important aspect is the ability to define the particular conditions under which a certain pattern or structure will be formed such that the products will be uniform. This clearly happens in Nature, but natural systems have been subject to trial and error for considerably longer than any experiment involving synthetic systems. 

---