Bias, definition

Type of Bias Definition 1 Important Associations Solutions... 

Whenever an economic evaluation is undertaken, a corresponding problem definition should be provided as the basis on which the evaluation is made. This definition, sometimes called an economic scope, should clearly differentiate between specifications that have actually been selected and features that have been assumed for the evaluation. In a comparison of alternatives, all of the assumptions, data, and conditions must be consistent, reaUstic, and devoid of bias. 

Definition and Uses of Standards. In the context of this paper, the term "standard" denotes a well-characterized material for which a physical parameter or concentration of chemical constituent has been determined with a known precision and accuracy. These standards can be used to check or determine (a) instrumental parameters such as wavelength accuracy, detection-system spectral responsivity, and stability (b) the instrument response to specific fluorescent species and (c) the accuracy of measurements made by specific Instruments or measurement procedures (assess whether the analytical measurement process is in statistical control and whether it exhibits bias). Once the luminescence instrumentation has been calibrated, it can be used to measure the luminescence characteristics of chemical systems, including corrected excitation and emission spectra, quantum yields, decay times, emission anisotropies, energy transfer, and, with appropriate standards, the concentrations of chemical constituents in complex S2unples. 

Both approaches are subjected to criticisms and to errors. The stipulation of the model constitutes a bias in favour of one among several possible alternatives, and the conclusions will suffer if the choice was not appropriate. All the models, by definition, are subjected to failures of this kind, and it rests with the users to exert their acumen to deeide if the model is applicable to the case under examination. 

Figure 20 shows more definitively how the location and orientation of a hyperplane is determined by the projection directions, a and the bias, o- Given a pattern vector x, its projection on the linear discriminant is in the a direction and the distance is calculated as d(x ) / cf The problem is the determination of the weight parameters for the hyper-plane ) that separate different pattern classes. These parameters are typically learned using labeled exemplar patterns for each of the pattern classes. 

Accuracy is often used to describe the overall doubt about a measurement result. It is made up of contributions from both bias and precision. There are a number of definitions in the Standards dealing with quality of measurements [3-5]. They are only different in the detail. The definition of accuracy in ISO 5725-1 1994, is The closeness of agreement between a test result and the accepted reference value . This means it is only appropriate to use this term when discussing a single result. The term accuracy , when applied to a set of observed values, describes the consequence of a combination of random variations and a common systematic error or bias component. It is preferable to express the quality of a result as its uncertainty, which is an estimate of the range of values within which, with a specified degree of confidence, the true value is estimated to lie. For example, the concentration of cadmium in river water is quoted as 83.2 2.2 nmol l-1 this indicates the interval bracketing the best estimate of the true value. Measurement uncertainty is discussed in detail in Chapter 6. 

When evaluating uncertainty, it is important to understand the distinction between empirical and non-empirical methods, as this influences how the uncertainty is evaluated. In the case of non-empirical methods, any bias in the results which is due to the method of analysis or, for example, a particular sample type, needs to be considered as part of the uncertainty evaluation process. For example, if a method was intended to determine the total amount of cadmium present in a soil sample, but for some reason only 90% of the cadmium present was extracted from the sample, then this 10% bias would need to be accounted for in the uncertainty estimate. One approach would be to correct results to take account of the bias. However, there would be an uncertainty associated with the correction as there will be some uncertainty about the estimate of the bias. For empirical methods, the method bias is, by definition, equal to zero (the method defines the result obtained). However, when evaluating the uncertainty associated with results obtained from an empirical method, we still need to consider the uncertainty associated with any bias introduced by the laboratory during its application of the method. One approach is to analyse a reference material that has been characterized by using the same empirical method. If no suitable reference material is available, then any bias associated with carrying out the individual stages of the method in a particular laboratory will need to be evaluated. 

Rigorous correction for instrumental mass bias is required if the precision of an isotope ratio measurement needs to be greater than l%o per mass unit. This concept is well illustrated by the definitive Ca isotope work of Russell et al. (1978), which used a double-spike approach. Prior to the Ca isotope investigation of Russell et al. (1978), natural mass-dependent Ca... 

Fisher polynomials can be used only within the T range for which they were created. Extrapolation beyond the T limits of validity normally implies substantial error progression in high-F entropy and enthalpy calculations. For instance, figure 3.4 compares Maier-Kelley, Haas-Fisher, and Berman-Brown polynomials for low albite. As can be seen, the first two interpolants, if extended to high T, definitely exceed the Dulong and Petit limit. The Berman-Brown interpolant also passes this limit, but the bias is less dramatic. 

Defining the word "sensor" is far from easy. Because a sensor is currently regarded by many as the magic key to a number of doors, the term is very often used improperly. No doubt, the personal bias of the many authors who have provided a definition for "sensor" has fostered the wide variety of existing interpretations. Thus, some consider a sensor to be "a wavelength" at which the absorbance of an analyte or reaction product is measured. Others have an even vaguer idea and call an FIA assembly a sensor, for example. 

The basis of this definition is that a fuel cell run by the products from the photoelectrolysis cell supplies a part of its output to the photoelectrolysis cell as electrical bias. The combined system must have a significant positive energy output to be considered as useful. 

The definition of a per-protocol set of subjects allows us to get closer to the scientific question by including only those patients who comply with the protocol to a defined extent. The per-protocol set, like the full analysis set, must be prespecified in the protocol and then defined at the patient level at the blind review, following database lock, but before breaking the blind. It must be noted, however, that the per-protocol set is subject to bias and further, tends to overestimate the treatment effect. For this reason it is usually used only as a secondary analysis, supportive hopefully of the findings based on the full analysis set. 

One very simplistic way of handling missing data is to remove those patients with missing data from the analysis in a complete cases analysis or completers analysis. By definition this will be a per-protocol analysis which will omit all patients who do not provide a measure on the primary endpoint and will of course be subject to bias. Such an analysis may well be acceptable in an exploratory setting where we may be looking to get some idea of the treatment effect if every subject were to follow the protocol perfectly, but it would not be acceptable in a confirmatory setting as a primary analysis. 

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