Representative Bias

Fsa = spectral shape factor representing bias and variability in ground motion and associated ground response spectra... 

Fs = damping factor representing bias and variability in response due to the difference between actual damping and damping assumed in design... 

Representative bias refers to the tendency by which an individual categorizes a situation based on a pattern of previous experiences or beliefs about the scenario. Representative bias can be useful when trying to make a quick decision, but it can also be limiting because it leads to close-mindedness, such as in stereotypes. 

Representative bias manifests errors in judgment as follows ... 

Representative bias can be alleviated with the use of Bayesian statistics, named after The Reverend Thomas Bayes, who made an early contribution to understanding the logic of how people should change their minds in the light of evidence. Bayesian statistics allows a decision-maker discipline in the decision-making process by ... 

Clearly, this choice of a reference set of organic reactions is arbitrary, not necessarily representative of the whole set of organic reaction types described in the literature, and therefore not free from bias. However, it does give some indication of the relative importance of the various reaction schemes. It is quite clear that the reaction scheme shown in Figure 3-13 (R1 of Table 3-3) comprises the majority of organic reactions in most compilations of reactions it will account for more than 50 % of all reactions. 

The presence of errors within the underlying database fudher degrades the accuracy and precision of the parameter e.stimate. If the database contains bias, this will translate into bias in the parameter estimates. In the flash example referenced above, including reasonable database uncertainty in the phase equilibria increases me 95 percent confidence interval to 14. As the database uncertainty increases, the uncertainty in the resultant parameter estimate increases as shown by the trend line represented in Fig. 30-24. Failure to account for the database uncertainty results in poor extrapolations to other operating conditions. 

Representativeness can be examined from two aspects statistical and deterministic. Any statistical test of representativeness is lacking becau.se many histories are needed for statistical significance. In the absence of this, PSAs use statistical methods to synthesize data to represent the equipment, operation, and maintenance. How well this represents the plant being modeled is not known. Deterministic representativeness can be answered by full-scale tests on like equipment. Such is the responsibility of the NSSS vendor, but for economic reasons, recourse to simplillcd and scaled models is often necessary. System success criteria for a PSA may be taken from the FSAR which may have a conservative bias for licensing. Realism is more expensive than conservatism. 

The ANN model had four neurones in the input layer one for each operating variable and one for the bias. The output was selected to be cumulative mass distribution thirteen neurones were used to represent it. A sigmoid functional... 

Fig. 34—(a) Representative SEM micrograph, (b) surface profiles of the scratch tracks, and (c) the evolutions of normal load and friction force between the tip and the film of the DLC film deposited at -90 V bias. 

Reference value A value reported on an SRM Certificate of Analysis and/or a Reference Material (RM) Report of Investigation that represents the best estimate of the true value where all known or suspected sources of bias have not been fully investigated by NIST. The value has an associated uncertainty that may not include all sources of uncertainty and may represent only a measure of the precision of the measurement method(s). 

Other authors used a simple 2 standard deviation criteria or an outlier test (F-test) to check for significant differences between within-bottle and between-bottle results (Martin-Esteban et al. 1997 Quevauviller et al. 1995). The degree of homogeneity of elements and compounds in the materials tested in these studies does not seem to be adequately described and, hence, the asigned uncertainties in the mean values may represent only the bias between the analytical methods used in the certification. 

In Table I are shown representative data concerning plasma deposition of a-C(N) H films, produced by different methods and under different gaseous mixtures and deposition parameters. In this table are displayed data on chemical composition (maximum N content, and range of variation of H content), deposition details (deposition pressure, self-bias, and atmosphere composition), and the method used for chemical composition determination. 

But there are those in my lab who believe that the excitation is being seen by a bias toward large cells and that they represent a large cell population in the neostriatum. I don t necessarily believe that. I don t know why, in the anesthetized animal, you can flip a nerve cell that is inhibited by amphetamine by increasing the dose. It has been postulated that the excitation is related to the occurrence of both the stereotyped behaviors, and that this may be provoked at doses that produce neurotoxicity. We have also done a number of studies looking at the neurotoxicity of amphetamine administration in animals, most of which replicate Lou Seiden s work. 

The goal of groundwater sampling is to obtain samples that are representative of the aquifer being investigated and to minimize the bias introduced by the methods employed to collect those samples. The purpose of the collection of representative samples is to investigate whether pesticide products in groundwater are present and, if present, at what concentration. 

If the mathematical model represents adequately the physical system, the error term in Equation 2.3 represents only measurement errors. As such, it can often be assumed to be normally distributed with zero mean (assuming there is no bias present in the measurement). In real life the vector e, incorporates not only the experimental error but also any inaccuracy of the mathematical model. 

Validate routine methods, i.e., define the conditions under which the assay results are meaningful.115 To do that, one must select samples that are truly representative of the product stream. This may be a difficult task when the process is still under development and the product stream variable. The linearity of detector response should be defined over a range much broader than that expected to be encountered. Interference from the sample matrix and bias from analyte loss in preparation or separation often can be inferred from studies of linearity. Explicit detection or quantitation limits should be established. The precision (run-to-run repeatability) and accuracy (comparison with known standards) can be estimated with standards. Sample stability should be explored and storage conditions defined. 

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