Sources of experimental error

Design of experiments. When conclusions are to be drawn or decisions made on the basis of experimental evidence, statistical techniques are most useful when experimental data are subject to errors. The design of experiments may then often be carried out in such a fashion as to avoid some of the sources of experimental error and make the necessary allowances for that portion which is unavoidable. Second, the results can be presented in terms of probability statements which express the reliabihty of the results. Third, a statistical approach frequently forces a more thorough evaluation of the experimental aims and leads to a more definitive experiment than would otherwise have been performed. 

The Thermodynamics Research Center staff have assigned an uncertainty value to each observed and recommended density value listed in the tables. The tme value of the property has a 95% probability of being in the range covered by + or - the uncertainty about the reported value. Assignment of uncertainty is a subjective evaluation based upon what is known about the measurement when the value is entered into the database, and includes the effects of all sources of experimental error. The errors have been propagated to the listed density at the reported temperature. Uncertainties reported by the investigators are considered but not necessarily adopted. Often, investigators report repeatability, but they usually do not provide uncertainty. 

The challenge is then to achieve the same degree of accuracy in the derived values of the experimental electron density. Recent studies have shown that in some cases this is indeed within the reach of the present-day modelling techniques [3-5]. When the major sources of experimental error have been corrected for the typical root mean square electron density residual can reach values as low as 0.05 e A-3, with maxima below 0.20eA-3 in absolute value. The observed residuals are usually due to the... 

The various sources of experimental error were also considered. From Eq. (6), we should expect an accuracy of approximately 2% for AD/D since the errors in pH, a and H3Cit cancel in considering the 0/ values for two elements in the same aqueous phase. The relative error in 0/ can be estimated to be 5 % from Eq. (6). It appears of course that the absolute error in 0/exceeds this value. 

Were there any sources of experimental error What could you improve if you did this investigation again ... 

Selected entries from Methods in Enzymology [vol, page(s)] Theoretical aspects, 76, 354-356 diagmagnetic contribution, 76, 358-359 experimental methods, 76, 356-360 Faraday balance technique, 76, 360-361 Gouy technique, 76, 357, 360 instrumentation, 76, 360-369 oxygen contribution, 76, 360, 368 sources of experimental errors, 76, 359-360 SQUID magnetometer use, 76, 364-365 thermal equilibria, 76, 358, 370 thermal expansion, 76, 358. 

It is essential to take into account a number of potential sources of experimental error in the determination of an adsorption isotherm. In the application of a volumetric technique involving a dosing procedure it must be kept in mind that any errors in the measured doses of gas are cumulative and that the amount remaining unadsorbed in the dead space becomes increasingly important as the pressure increases. In particular, the accuracy of nitrogen adsorption measurements at temperatures of about 77 K will depend on the control of the following factors ... 

This collection of limiting factors clearly influences the quality of the model and its predictive power. In view of these sources of inconsistencies and errors in determining the intestinal absorption in humans, it is impressive that the quality of the model is as good as it is. In particular this model was shown to be of particular use as general model in earlier phase for classification of molecules. Any significant improvement is likely not to come from statistical or computational methods but from more consistent data on a broader collection of drug-like molecules preferably by an in vitro system eliminating some sources of experimental errors. 

Permeability, P, and difiusion coefficient, D, were obtained by performing a two-parameter least squares fit of the experimental flux data to equation (2). The solubility, S, was obtained from the relationship P = DS. The methodology of these oxygen barrier measurements, data analysis, and the sources of experimental error were previously described in detail elsewhere (7/). 

As comprehensively reviewed by Lipson and Guillet (1), inverse gas chromatography (IGC) has been used as a convenient tool to study the thermodynamic properties of polymeric systems. Despite its wide usage, all experimental and theoretical factors in this technique are not fully understood. Loading determination, usually done by means of extraction or calcination, has been considered to be the most significant source of experimental error (2.). Other factors, such as concentration effects associated with large injection sizes, slow diffusion of solute probe molecules in the stationary phase, and adsorption of probes onto the liquid-support interface, may also af-... 

The observed frequencies with 3, 5, 7 and 14 different and equally probable random error sources are shown in Fig. 3,6c-f. With many different sources of experimental error, it is seen that the frequency of the experimental response data can be approximately described by the bell-shaped curve in Fig. 3.6g. 

The most widely used method for obtaining experimental proton anisotropies has been that of liquid crystal NMR measurements. This method has several inherent sources of experimental error, which, in the case of proton shielding, often overshadow the resulting anisotropy. Many of the analyses of liquid crystal proton spectra have been based on the observed shift between the nematic and isotropic phases. Such a procedure relies on the assumption that the solvent effects are the same in both the ordered and isotropic phases.3... 

The sources of experimental error are rarely examined in detail and the statistical treatment of observational error is generally simplistic and based on convenient assump-... 

Experimental design is a large topic and we can only mention several of the important issues here. To keep this discussion focused on parameter estimation for reactor models, we must assume the. reader has had exposure to a course in basic statistics [4]. We assume the reader understands the source of experimental error or noise, and knows the difference between correlation and causation. The process of estimating parameters in reactor models is part of the classic, iterative scientific method hypothesize, collect experimental data, compare data and model predictions, modify hypothesis, and repeat. The goal of experimental design is to make this iterative learning process efficient. 

A method based on the comparison of experimental and calculated kinetic dependencies of the dynamic surface tension can be more precise in comparison with the use of Eq. (5.253) [77, 85, 89, 92, 93]. Mitrancheva et al. presented the most detailed data and compared calculated dynamic surface tension with results obtained for solutions of TRITON X-100 using three different experimental methods the inclined plate, the oscillating jet and the maximum bubble pressure methods [93]. The inclined plate method yielded values of i2 different from the results of the two other techniques. This discrepancy is probably connected with the differences in the attainable surface age. Thus the inclined plate method can be used only at relatively high surface life times when the surface tension tends asymptotically to equilibrium, and when the accuracy of determination of i2 decreases. In addition the insufficiently investigated peculiarities of the liquid flow along the inclined plane can be another source of experimental errors [93]. 

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