Transcription error, examples

As shown by Examples 4.1 and 4.2, the mean and median provide similar estimates of central tendency when all data are similar in magnitude. The median, however, provides a more robust estimate of central tendency since it is less sensitive to measurements with extreme values. For example, introducing the transcription error discussed earlier for the mean only changes the median s value from 3.107eto3.112e. 

In this what if, the pure component spectra for Example 1 are estimated with a transcription error in the concentration matrix. Assume that for sample 3, component A was entered as 0.20 instead of 0.10 (see Table 5-7). Because the error is only for component A, it might be imagined that the error will only affect the estimation of the pure spectrum for this component. This is not correct, because the entire C matrix is used to estimate all of the pure spectra. 

This last part or phase is traditionally thought of as "the audit." It can be accomplished in two basic ways either using a random number statistical approach or by a percentage or line approach. In conducting an audit, it is important to remember that some types of errors (usually the small ones) are random, for example, a simple transcription error at the end of a long calculation, while others follow patterns and can have a cumulative impact, such as an unacceptable calibration curve or even sample mix-up. 

For the final activity calculations a software package called LSC Plus (supplied by Raddec Ltd.) is used. This software was developed to avoid using spreadsheet calculations, to minimize transcription errors and to save time. The counting of samples on Quantulus consists of 3 repeat counts. For example, when counting a batch of 20 samples, it produces 60 individual results you then have to manually enter into a spreadsheet. It automatically imports those data and calculates final results and total expanded uncertainties together with the LODs (Fig. 5). It has built-in quality control features introduced to assist in the requirements of ISO17025. 

Laboratory safety data can be erroneous, and this must always be considered when abnormalities are reported. There are numerous sources of error, which may be related to the study subject (for example, self-medication, certain foods, undue exercise), sample collection technique, storage and transport, the analytical technique used (for example, high variability, inappropriate reference ranges, interfering substance in the sample), or to the report (for example, transcription errors). Some of the sources of error are listed in Table 6.4. When there is doubt about the validity of the test, it should be repeated and, if necessary, at a different laboratory. 

Integrative and Advanced Exercises 52. 2.9X10 g/mol. This is a minimum value because it is assumed that 1 Ag ion is all that is necessary to denature each protein molecule. 56.9.71 58. Nonapeptide Arg-Pro-Pro-Gly-Phe-Ser-Pro-Phe-Arg. 59. One example for the required sequence would be AGA GCA CAA CGA. The redundancy enables the organism to produce a given amino acid in several ways in case of transcription error. 62. Met-Ser-Met-Met-Gly. 65a. 9.3x10-5 65b. I.SXIO ... 

Prescription errors are unintentional mistakes in the prescription, transcription, dispensing, and administration of medications. The patient either receives the medication incorrectly or fails to receive it altogether. Some prescription errors include wrong patient, incorrect medication, inappropriate dose, wrong time, wrong route of administration, and wrong rate of administration. For example, the profile of a patient shows that he is allergic to codeine, and yet he receives Tylenol 3 by an error. 

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