Experimental procedure statistical analyses

Consistent Data-Recording Procedures. Clear procedures for recording all pertinent data from the experiment must be developed and documented, and unambiguous data recording forms estabUshed. These should include provisions not only for recording the values of the measured responses and the desired experimental conditions, but also the conditions that resulted, if these differ from those plaimed. It is generally preferable to use the values of the actual conditions in the statistical analysis of the experimental results. For example, if a test was supposed to have been conducted at 150°C but was mn at 148.3°C, the actual temperature would be used in the analysis. In experimentation with industrial processes, process equiUbrium should be reached before the responses are measured. This is particularly important when complex chemical reactions are involved. 

In recent decades, the development of chemical, biochemical, and biological techniques has allowed the creation of analytical tools which can be used to facilitate the identification of the mechanisms involved in neoplastic transformation. Animal models remain, however, the most widely used approach of investigation. Cancer bioassays are usually conducted in rodents (rats and mice) and the experimental protocol takes 18-24 months and it is followed by extensive histopathological and statistical analysis. The procedure is time and... 

Even the least pernickety experimenter recognizes that all the basic details of laboratory procedures have to be planned well before putting hand to test-tube and yet it seems normal practice (especially among academics) that statistical analysis is the merest after-thought. The author recently reviewed a protocol for a postgraduate project that blithely asserted when all the data have been collected, a statistician will be consulted to show the results are significant . 

Finally, a number of Supplementary Chapters describe important topics such as endotoxin testing, statistical analysis of experimental results and guides for the nomenclature of complex natural or semi-synthetic drugs. Supplementary Chapter III F describes the validation of analytical procedures and contains a glossary of terms and their definitions, such as specificity, accuracy, precision, detection limit, etc. 

Pore structure analysis methods based upon realistic disordered microstructures may be classified into two types. In one approach, the experimental procedures used to fabricate the material are reproduced, to the greatest extent possible, via molecular simulation, and the resulting amorphous material structure is then statistically analyzed to obtain the desired structural information. In the other approach, adsorbent structural data (e.g., smaU-angle neutron scattering) is used to construct a model disordered porous structure that is statistically consistent with the experimental measurements. As in the first approach, molecular simulations can then be carried out using the derived model structure to obtain the structural characteristics of the original adsorbent. 

In the interpretation of the numerical results that can be extracted from Mdssbauer spectroscopic data, it is necessary to recognize three sources of errors that can affect the accuracy of the data. These three contributions to the experimental error, which may not always be distinguishable from each other, can be identified as (a) statistical, (b) systematic, and (c) model-dependent errors. The statistical error, which arises from the fact that a finite number of observations are made in order to evaluate a given parameter, is the most readily estimated from the conditions of the experiment, provided that a Gaussian error distribution is assumed. Systematic errors are those that arise from factors influencing the absolute value of an experimental parameter but not necessarily the internal consistency of the data. Hence, such errors are the most difficult to diagnose and their evaluation commonly involves measurements by entirely independent experimental procedures. Finally, the model errors arise from the application of a theoretical model that may have only limited applicability in the interpretation of the experimental data. The errors introduced in this manner can often be estimated by a careful analysis of the fundamental assumptions incorporated in the theoretical treatment. 

All experimental procedures concerning DNA extraction, polymerase chain reaction (PCR), cloning, nucleotide sequencing, molecular phylogenetic analyses, and statistical analysis were conducted as described (Nikoh and Fukatsu, 2000). 

Experimental design and methods Schedule of assessments Subjection selection criteria Screening procedures for entry Study parameters Trial medication Premature withdrawal Subject replacement policy Criteria for excluding data Statistical analysis plans Signatures... 

Clearly the design of the calibration procedure and the statistical analysis of the data are both Important considerations. Questions which require attention are conveniently divided into two groups those pertaining to the experimental design and those pertaining to the statistical analysis. Design questions Include ... 

In the case where there are k populations under consideration, and the test of hypothesis is equality of population means, a different type of procedure is necessitated. This area of statistics is called experimental design or analysis of variance. This topic is covered in Chapter 85 of this Handbook. 

The procedures just outlined rely exclusively on statistical analysis of rate data. Such an exercise converges to the most plausible model, not necessarily the only acceptable one. It is possible to combine the statistical methods with other experimental methods to obtain a more unique model. One such method is to perturb the system by changing any of the parameters (such as the reactant concentration, flow rate, temperature, pressure, etc.), and determine the time taken to regain steady state. This gives important information on the rates of... 

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