Systematic bias

Measurements can contain any of several types of errors (1) small random errors, (2) systematic biases and drift, or (3) gross errors. Small random errors are zero-mean and are often assumed to be normally distributed (Gaussian). Systematic biases occur when measurement devices provide consistently erroneous values, either high or low. In this case, the expected value of e is not zero. Bias may arise from sources such as incorrect calibration of the measurement device, sensor degradation, damage to the electronics, and so on. The third type of measurement... 

Measurements of exposure parameters—for example, the indoor air concentrations of chemicals, time-activity patterns and the chemical composition of consumer products—will as a rule be subject to errors inherent in the methodology used, such as errors in the analytical methods used to measure chemical concentrations or inaccuracies in the survey data due to incorrect reporting by the participants. These errors may be categorized as random or systematic (biased) errors ... 

A particular type of within-array analysis is the so called self-self hybridization [9], in which two dyes are used to label the same RNA species, so that the fluorescence values acquired by the scanner for each gene is supposed to be the same for the two channels. This approach allows the identification of the variability which depends only on systematic biases or on stochastic processes. Some authors suggest the performance of some self-self hybridization for each experiment, to establish an error model used to correct data derived from experimental measurements. 

Carswell and Wickens (Carswell, 1992 Carswell Wickens, 1988 1990) have demonstrated effects of perceptual analysis of integrality on graph comprehension, and others have shown systematic biases in interpretation or memory dependent on the form of graphic displays (Gattis Holyoak, 1996 Levy, Zacks, Tversky Schiano, 1996 Schiano Tversky, 1992 Shah Carpenter, 1995 Spence Lewandowsky, 1991 Tversky Schiano, 1989). 

In addition to chance, systematic biases can also affect the relationship between an exposure and disease. Biases lead to an incorrect estimate of the relationship between the exposure and disease that is an incorrect measure of the relative risk. Some biases will result in an effect being observed (i.e., statistically significant RR) when there is not a causal relationship, whereas other biases will result in obscuring a causal relationship between exposure and disease (refer to as biasing toward the null hypothesis). In an individual study, biases can be introduced during the selection of the subjects, follow-up of disease status, or exposure assessment. Biases can also occur in the evaluation of a causal relationship across studies. 

Our knowledge of crustal composition obviously depends on the number and spatial coverage of the available measurements. Uranium and thorium concentrations can now be determined routinely using modern techniques, and the number of data published each year is very large. Yet there are several systematic biases in the data, and these must be borne in mind. 

In practical blending processes, one cannot obtain arbitrary quantities of pristine data as one can using particle-dynamic simulations, and one must settle for sampling a static bed, as mentioned previously. In such a case, it is especially important to understand sampling limitations and systematic biases. A common means of obtaining samples in a tumbler is by the use of a scoop or thief sampler. These samplers are inserted into the bed and extract samples from its... 

Even though there are many positive aspects of microscopy, there are also some disadvantages. In particular, microscopy is a very slow and tedious analysis method if manual counting is done. It can take a long time to count the 200-500 particles that are necessary for a statistically valid analysis. In addition, manual counting requires an experienced operator. If the operator is not well trained, inaccurate results can occur due to systematic biases that are common to the human eye (9,40). There is also a reproducibility issue between different operators. 

Various technologies have been used to measure plasma lipids and lipoproteins and lipoprotein subfractions, including enzymatic, immunochemical, and chemical precipitation reagents, and physical methods, such as ultracentrifugation, electrophoresis, column chromatography, and others. Such methods have been reviewed extensively. As mentioned earlier, however, the cholesterol content of any particular lipoprotein class can vaiy somewhat from individual to individual. Moreover, although different methods of lipoprotein separation may produce similar lipoprotein fractions, they usually do not produce identical fractions, giving rise to systematic biases between methods that purport to measure the same component. The present discussion focuses primarily on methods and procedures commonly used in clinical practice for lipid and lipoprotein measurements. 

Any clinical trial may be subject to unanticipated, undetected, systematic biases. These biases may operate despite the best intentions of sponsors and investigators, and may lead to flawed conclusions. In addition, some investigators may bring conscious biases to evaluations. 

Application of scaling or shifting techniques to reduce systematic biases... 

The reactivity uncertainties due to systematic biases were not included in Table 4.2-8. A systematic bias is defined as a known effect, which affects reactivity, but which was not, for various practical reasons, included in analytical calculations. As noted earlier, the systematic biases due to added fissile loading and neglect of graphite impurities have already been taken into account in the expected nominal reactivity requirements. 

When this happens it is unlikely that methodological problems or systematic biases can influence the results of the studies conducted in different contexts and different study designs. The studies included in this meta-analysis usually controlled for such items as geographical location and date of birth, however, other potential confounding factors such as maternal age, alcohol, and smoking that could lead to subsequent problems in outcome presentation were not consistently reported. 

This does not automatically imply that replicate x-ray spectrometric analytical results are normally distributed since systematic biases may exist. 

Because Eq. (6.26) is very simple, an on-line minicomputer can solve for Nq and Nr very quickly. Providing the peak shapies and positions in the reference spectra are identical to those in the composite spectrum, the method is relatively free of systematic biases in estimating the net intensities. The technique can also be applied to overlap between two line series. For example, the Mn Ky3 lies under the Fe Kcr on energy dispersive spectrometers. The Mn Ka peak area can be used to subtract the Mn interference from the Fe Ka line. However, a word of caution is in order when using Ka/Kj8 or hoc/Lfi ratios for resolving interferences. Occasionally the composite specimen contains an absorption edge between the two lines, which is not present in the pure element reference spectra. Thus, the Ka/Kfi or La/Lfi ratios are different in the composite specimen than in the pure element reference standards. 

Although derived in the context of biomolecular handedness, this formula is generally valid for any system that breaks a two-fold symmetry, such as mirror inversion. Using this formula, it is possible to understand the extraordinary sensitivity of bifurcation to small systematic biases that favor one enantiomer by increasing its production rate. For example, it can be estimated that the chiral asymmetry of the electroweak interaction can create differences of the order of one part in 10 between the enantiomers. Application of the above theory shows that if the autocatalytic production rate of the chiral molecules is faster than the racemization rates, then for a period in the range 10" to 10 years, the enantiomer favored by the electroweak force will dominate [16]. For such a scenario, there is currently no experimental evidence to show us how chiral autocatalysis with the required properties can originate in prebiotic chiral molecules. 

The examination and comparison of data generated from independent laboratory experiments are important aspects in the establishment of the most useful correlations. Multiple data sets provide input to the scientific judgment needed to determine realistically quantitative standard uncertainties. The use of different experimental techniques and data from separate laboratories is needed to discover systematic biases which may be entrenched in an established experimental program. 

The amplitudes of the experimental auto-correlation curves were corrected for afterpulsing and for background to avoid systematic biases (28), The following model equation was used for the FCS analysis of these three diffusing species ... 

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