Error examples

Because they are weak acids or bases, the iadicators may affect the pH of the sample, especially ia the case of a poorly buffered solution. Variations in the ionic strength or solvent composition, or both, also can produce large uncertainties in pH measurements, presumably caused by changes in the equihbria of the indicator species. Specific chemical reactions also may occur between solutes in the sample and the indicator species to produce appreciable pH errors. Examples of such interferences include binding of the indicator forms by proteins and colloidal substances and direct reaction with sample components, eg, oxidising agents and heavy-metal ions. 

Simplify—Design processes and facilities that eliminate unnecessary complexity and that are tolerant of human error. Example Design piping to permit gravity flow of hazardous materials in a plant, eliminating the need for pumps, which can leak. 

Array must have an equal number of rows and columns. If any cells in array do not contain numbers, MINVERSE returns VALUE . If MDETERM for the array returns 0, the array cannot be inverted MINVERSE will return NUM error. Example See Chapter 9 for details. 

Finally, Dr. Frank Hardcastle (TPL, Inc., Albuquerque, NM) and Professor Israel Wachs (Lehigh University) discuss the application of Raman spectroscopy to the study of heterogeneous catalysts. They review the theory, instrumentation, and sampling techniques of this approach. They also include a valuable discussion of its limitations and possible sources of error. Examples of both zeolite and metal-oxide spectra are provided and analyzed. 

Immunoassays offer much potential for rapid screening and quantitative analysis of pesticides in food and environmental samples. However, despite this potential, the field is still dominated by conventional analytical approaches based upon chromatographic and spectrometric methods. We examine some technical barriers to more widespread adoption and utilization of immunoassays, including method development time, amount of information delivered and inexplicable sources of error. Examples are provided for paraquat in relation to exposure assessment in farmworkers and food residue analyses molinate in relation to low-level detection in surface waters and bentazon in relation to specificity and sensitivity requirements built in to the immunizing antigen. A comparison of enzyme-linked immunosorbent assay (ELISA) results with those obtained from conventional methods will illustrate technical implementation barriers and suggest ways to overcome them. 

For this reason, we pay close attention to minimizing sources of error. Examples of common sources of error might be contamination or an improperly mixed buffer. In addition, one should remember that scientists are humans, and human error is always a possibility. 

The invertible transformation stage uses a different mathematical basis of features in an attempt to decorrelate the data. The resulting data will have a set of features that capture most of the independent features in the original data set. Typical features used include frequency and spatial location. The transformation is nearly loss-less as it is implemented using real arithmetic and is subject to (small) truncation errors. Examples of invertible transforms include the discrete cosine transform (DCT), the discrete wavelet transform (DWT) and the wavelet packet transform (WPT). We will investigate these transforms later. 

Type C alarm about a runtime error which has a predictable outcome. The analyzer continues the analysis by overapproximating all possible results, including the effect of the error. Examples are integer overflows, invalid shifts, invalid cast operations. 

Error identifier techniques use prompts or questions to aid the analyst in identifying potential errors. Examples of error identifier prompts include Could the operator fail to carry out the act in time or Could the operator carry out the task too early or Could the operator carry out the task inadequately (Kirwan, 1994). The prompts are linked to a set of error modes and reduction strategies. Although these techniques attempt to remove the reliability problems associated with taxonomy-based approaches, they add considerable time to the analysis because each prompt must be considered. One example of an error identifier HEl technique is the Human Error Identification in Systems Tool (HEIST) approach (Kirwan, 1994). 

Human errors - examples of human errors that may result in a false fire alarm include the following ... 

Inspection of Eqs. 5.111 through 5.119 shows thatx and tp are dependent variables once a value of S r is chosen. In principle, xp can be eliminated from Eq. 5.111 by use of Eqs. 5.108 and 5.110 ftirough 5.119. Then a v ue of can be found for each value of 5 between 5,, and. However, this sqiproach is sensitive to sniiall numericd errors. Example 5.10 illustrates the solution of Eq. 5.111 when an approximation technique is used. 

However, other errors can result in consistently high or low values being recorded. These are called systematic errors. Examples would be reading the volume of liquid... 

Human Error Probability Description of human interaction and error Example foctots for a fiicflity specific adjustment... 

Systematic errors are repeatable, consistent errors. Examples include a plastic centimeter ruler that has been distorted by sitting in the sun, and the fabled butcher with his thumb on the scale. Systematic errors are always present to some extent in our measuring instruments. They may be due to consistently faulty technique for example, the parallax effect in reading a scale. The word accurate in its technical sense refers to measurements with small systematic errors (Beers, 1957 Leaver and Thomas, 1974). Systematic errors can be reduced by more careful work or better calibration. 

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