Data interpretation types

Additional examples of variability in data collection (which, in turn, affects data interpretation) include questionnaires and physical exam forms. Questionnaires often utilize open-ended questions that allow great variability in the type and extent of adverse event information gathered. Physical exam forms—even when designed in a checklist format—may elicit variable collection of adverse event data what is a serious event to one clinician may not be serious to another. 

In this publication, the purpose of data analysis is to drive toward data interpretation that consists of assigning various types of labels. These label... 

As analytical chemists, we are often called upon to participate in studies that require the measurement of chemical or physical properties of materials. In many cases, it is evident that the measurements to be made will not provide the type of information that is required for the successful completion of the project. Thus, we find ourselves involved in more than Just the measurement aspect of the investigation —we become involved in carefully (re)formulating the questions to be answered by the study, identifying the type of information required to answer those questions, making appropriate measurements, and interpreting the results of those measurements. In short, we find ourselves involved in the areas of experimental design, data acquisition, data treatment, and data interpretation. 

These four areas are not separate and distinct, but instead blend together in practice. For example, data interpretation must be done in the context of the original experimental design, within the limitations of the measurement process used and the type of data treatment employed. Similarly, data treatment is limited by the experimental design and measurement process, and should not obscure any information that would be useful in interpreting the experimental results. The experimental design itself is influenced by the data treatment that will be used, the limitations of the chosen measurement process, and the purpose of the data interpretation. 

The occurrence of spontaneous anomalies is common in laboratory animals. The incidence and type of externally visible spontaneous anomalies in common laboratory strains vary over time in different laboratory animal populations, so historical control data, although extremely valuable and essential for satisfactory interpretation of studies, may also be misleading (Palmer, 1977 Szabo, 1989). Historical data from recent years provide useful background information for an experiment with a particular animal colony, but concurrent or very recent data are also necessary for data interpretation. 

The right-hand panel of Fig 1.1 illustrates an opposed-flow diffusion-flame arrangement. Here the fuel and oxidizer flows are separated, only coming together at the flame. Both premixed and nonpremixed flames find use in practical combustion devices. Thus it is important to model and understand the behaviors of both types of flames, as well as combinations. The opposed contraction nozzles illustrated in the figure lead to a desirable flow similarity, which facilitates modeling and data interpretation. 

Unless the cooling system operation is particularly stable, inhibitor reserve testing should be carried out daily, using the best type of field equipment that can be afforded. A portable colorimeter or spectrophotometer is recommended. The results should be graphed for easy viewing and data interpretation. 

There are several sample digestion procedures used in elemental analysis. All of them use strong oxidizers (nitric acid, hydrochloric acid, and hydrogen peroxide) to solubilize environmentally available metals. The following distinctions between different types of elemental analysis digestion procedures are important for the planning of data collection and in data interpretation. 

Several reactor types have been described [5, 7, 11, 12, 24-26]. They depend mainly on the type of reaction system that is investigated gas-solid (GS), liquid-solid (LS), gas-liquid-solid (GLS), liquid (L) and gas-liquid (GL) systems. The first three arc intended for solid or immobilized catalysts, whereas the last two refer to homogeneously catalyzed reactions. Unless unavoidable, the presence of two reaction phases (gas and liquid) should be avoided as far as possible for the case of data interpretation and experimentation. Premixing and saturation of the liquid phase with gas can be an alternative in this case. In homogenously catalyzed reactions continuous flow systems arc rarely encountered, since the catalyst also leaves the reactor with the product flow. So, fresh catalyst has to be fed in continuously, unless it has been immobilized somehow. One must be sure that in the analysis samples taken from the reactor contents or product stream that the catalyst docs not further affect the composition. Solid catalysts arc also to be fed continuously in rapidly deactivating systems, as in fluid catalytic cracking (FCC). 

As many studies submitted to pesticide regulatory jurisdictions use in vivo methodologies, but an increasing number of studies employ in vitro techniques, this section addresses data interpretation challenges with both study types. Discussion... 

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