Experimental design limitations

Factors that affect the in-service corrosion rate by influencing the controlling thermodynamic and/or kinetic parameters that are not accounted for by the experimental design limit the usefulness of the test. Typical operational parameters of concern include, but are not limited to, exact material composition and forming techniques, service temperature, electrolyte chemistry, electrolyte velocity, time-of-wetness (wet/dry cycles), exposure duration, and mechanical forces (e.g., abrasion or stress). Often, neglect of the key... 

Each observation in any branch of scientific investigation is inaccurate to some degree. Often the accurate value for the concentration of some particular constituent in the analyte cannot be determined. However, it is reasonable to assume the accurate value exists, and it is important to estimate the limits between which this value lies. It must be understood that the statistical approach is concerned with the appraisal of experimental design and data. Statistical techniques can neither detect nor evaluate constant errors (bias) the detection and elimination of inaccuracy are analytical problems. Nevertheless, statistical techniques can assist considerably in determining whether or not inaccuracies exist and in indicating when procedural modifications have reduced them. 

Such programs generally concentrate on the technical parts of designing an experiment, and provide limited guidance on the important, softer aspects of experimental design stressed in this article. Also, most computer routines do not allow one to handle various advanced concepts that arise frequently in practice, eg, spHt plot and nested situations, discussed in the books in the bibhography. In fact, some of the most successful experiments do not involve standard canned plans, but are tailored to fit the problem at hand. 

Most of what we know about solvent effects is a result of studies in which the reactivity is compared in a series of solvents. There are two main types of experimental design in one of these the reaction is carried out in different pure solvents in the other design the reaction is studied in mixed solvents, often a binary mixture whose composition is varied across the entire range. Experimental limitations often... 

Models also can assist in experimental design and the determination of the limits of experimental systems. For example, it is known that three proteins mediate the interaction of HIV with cells namely, the chemokine receptor CCR5, the cellular protein CD4, and the viral coat protein gpl20. An extremely useful experimental system to study this interaction is one in which radioactive CD4, prebound to soluble gpl20, is allowed to bind to cellular receptor CCR5. This system can be used to screen for... 

The reliability of multispecies analysis has to be validated according to the usual criteria selectivity, accuracy (trueness) and precision, confidence and prediction intervals and, calculated from these, multivariate critical values and limits of detection. In multivariate calibration collinearities of variables caused by correlated concentrations in calibration samples should be avoided. Therefore, the composition of the calibration mixtures should not be varied randomly but by principles of experimental design (Deming and Morgan [1993] Morgan [1991]). 

Unlike conventional experimental designs which have independent variables, mixture designs possess variables which are interdependent in that the summation of the q component proportions must be unity. Typically, the individual component levels are restricted by lower (a ) and upper (bj) constraints imposed on the system by physical or chemical limitations of the formulation or by the selection of the level values by the formulator. These constraints are represented as ... 

A more subjective approach to the multiresponse optimization of conventional experimental designs was outlined by Derringer and Suich (22). This sequential generation technique weights the responses by means of desirability factors to reduce the multivariate problem to a univariate one which could then be solved by iterative optimization techniques. The use of desirability factors permits the formulator to input the range of property values considered acceptable for each response. The optimization procedure then attempts to determine an optimal point within the acceptable limits of all responses. 

Furthermore, there are two other aspects to the extrapolation problem one structural and one statistical. An illustrative example of these various cases can be found in a dataset of benzamides (S16.1). that one of the present authors (U.N.) published some time ago [44]. If one develops a PLS model based on the same descriptors and the same, experimental design-based, training set (compounds 1-16) augmented by compound 17 (Table 16.8) in order to prove the points raised above [the prediction limit (1.502) set to two times the overall RSD of the model (0.751) which roughly gives 95% confidence interval], one can observe the following with respect to predictions on the remaining test set compounds ... 

These types of experimental designs also have some limitations. The first is the exaggeration of the effect of missing or defective data on the results, as mentioned above. The second is the fact that until the entire plan is carried out, little or no information can be obtained. There are generally few, if any, intermediate results only after all the data is available can any results at all be calculated, and then all of them are calculated at once. This phenomenon is related to the first caveat until each piece of data is collected, it is missing from the experiment, and therefore the results that depend upon it cannot be calculated. 

Sections on matrix algebra, analytic geometry, experimental design, instrument and system calibration, noise, derivatives and their use in data analysis, linearity and nonlinearity are described. Collaborative laboratory studies, using ANOVA, testing for systematic error, ranking tests for collaborative studies, and efficient comparison of two analytical methods are included. Discussion on topics such as the limitations in analytical accuracy and brief introductions to the statistics of spectral searches and the chemometrics of imaging spectroscopy are included. 

In addition to rodent studies, regulatory guidelines for pharmaceuticals require that repeated dose safety studies of up to nine months (in the United States, six months elsewhere) in duration be conducted in a nonrodent species. The most commonly used nonrodent species is the dog, followed by the monkey and pig. Another nonrodent model used to a limited extent in systemic safety evaluation is the ferret. The major objectives of this chapter are (1) to discuss differences in rodent and nonrodent experimental design, (2) to examine the feasibility of using the dog, monkey, pig, and ferret in safety assessment testing, and (3) to identify the advantages and limitations associated with each species. 

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