Elimination sample size

Instead of calculations, practical work can be done with scale models (33). In any case, calculations should be checked wherever possible by experimental methods. Using a Monte Carlo method, for example, on a shape that was not measured experimentaUy, the sample size in the computation was aUowed to degrade in such a way that the results of the computation were inaccurate (see Fig. 8) (30,31). Reversing the computation or augmenting the sample size as the calculation proceeds can reveal or eliminate this source of error. 

Trace analysis is particularly attractive for SFE-HPLC since quantitative transfer of all analytes extracted to the chromatographic system becomes possible. At present, on-line SFE-HPLC appears to be feasible for qualitative analysis only quantitation is difficult due to possible pump and detector precision problems. Sample size restrictions also appear to be another significant barrier to using on-line SFE-HPLC for quantitative analysis of real samples. On-line SFE-HPLC has therefore not proven to be a very popular hyphenated sample preparatory/separation technique. Although online SFE-HPLC has not been quantitatively feasible, SFE is quite useful for quantitative determination of those analytes that must be analysed by off-line HPLC, and should not be ruled out when considering sample preparatory techniques. In most cases, all of the disadvantages mentioned with the on-line technique (Table 7.15) are eliminated. On- and off-line SFE-HPLC were reviewed [24,128]. 

This belief was further supported by the evidence of a correlation between the clinical response and REM sleep suppression as well as a temporal relationship between the onset of clinical response and REM sleep suppression. However, some of the later studies suggested that REM sleep suppression is not necessary for the antidepressant action (Gillin 1983). For example, some studies show evidence of no change or even an increase in REM sleep with the treatment of depression (Gillin et al. 2001). Recently, Landolt Gillin (Landolt and Gillin 2002) have also demonstrated that the antidepressant response to phenelzine treatment does not depend on elimination of REM sleep or inhibition of slow wave activity in non-REM sleep. However, the generalization of some of these studies is limited because of their small sample size. 

The first physical cause considered was the possibility of an inhomogeneous sample. To eliminate this as a possibility, the sample was ground before aliquots were taken. The sample size was still 10 g of sample per 100 ml of water. In this case, however, time constraints permitted only three replicate readings per flask. The results are shown in Table 9-3. 

The band profile obtained as a numerical solution of Equation 10.10 gives the concentration distribution as a function of the reduced time at the column end, i.e., at location x= 1, regardless of the column length. The band profile depends only on the column efficiency, the boundary conditions, the phase ratio, and the sample size (which is part of the boundary conditions). The mobile phase velocity has been eliminated from the mass balance equation and the apparent axial dispersion coefficient has been replaced by the plate number. 

There is a well-known theorem in statistics, called the Neyman-Pearson Lemma, which shows that, for a given sample size, it is simply not possible to eliminate these two mistakes we must always trade them off against each another. 

A randomized crossover design has theoretical appeal because it eliminates the largest source of experimental variance interindividual variability. This could significantly enhance statistical power and permit much smaller samples sizes to detect a treatment effect. Unfortunately, a crossover design is appropriate only in rare cases in psychopharmacology, namely in studies ... 

SPEs offer distinctive advantages over conventional liquid-liquid extractions. They are relatively fast, require small sample size, eliminate emulsification problems, provide the possibility of performing both cleanup and preconcentration of the extract in one analytical step, and offer high precision. Another great advantage of SPEs over liquid-liquid extractions is solvent savings. Unlike liquid-liquid extractions that often require hundreds of milliliters for single or multiple extractions, SPEs require only a few milliliters of solvents for analyte extraction and cleanup. 

Internal standard. A pure compound added to a sample in known concentration for the purpose of eliminating the need to measure the sample size in quantitative analysis and for correction of instrument variation. 

Matisova and co-workers11 have suggested that the need for a reproducible sample volume can be eliminated by combining the standard addition method with an in situ internal standard method. In the quantitative analysis of hydrocarbons in petroleum, they chose ethyl benzene as the standard for addition, but they used an unknown neighboring peak as an internal standard to which they referenced their data. This procedure eliminated the dependency on sample size and provided better quantitation than the area normalization method they were using. 

The criterion of sample size eliminated those laboratories that use the traditional Libby counting method. This situation was very unfortunate because... 

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