Bias, statistical sources

By definition, the experimental unit is the smallest unit randomly allocated to a distinct level of a treatment factor. Note that if there is no randomization, there is no experimental unit and (in nearly all cases) no experiment. Although it is possible to perform experiments without randomization, it is difficult to do well, and risky unless the experimental system is very well understood (7). Randomization is important for several reasons. Randomization changes the sources of bias into sources of variation in general, a noisy assay is better than a biased assay. Further, randomization allows estimates of variation to represent variation in the population this in turn justifies statistical inference (standard errors, confidence intervals, etc.). A common practice in cell-culture bioassay is to rotate among a small collection of layouts rather than use random allocation. Whereas rotation among a collection of layouts is certainly better than a fixed layout, it is both possible and practical to use carefully structured randomization on a routine basis, particularly when using a robot. 

Analytical chemists make a distinction between error and uncertainty Error is the difference between a single measurement or result and its true value. In other words, error is a measure of bias. As discussed earlier, error can be divided into determinate and indeterminate sources. Although we can correct for determinate error, the indeterminate portion of the error remains. Statistical significance testing, which is discussed later in this chapter, provides a way to determine whether a bias resulting from determinate error might be present. 

The "feedback loop in the analytical approach is maintained by a quality assurance program (Figure 15.1), whose objective is to control systematic and random sources of error.The underlying assumption of a quality assurance program is that results obtained when an analytical system is in statistical control are free of bias and are characterized by well-defined confidence intervals. When used properly, a quality assurance program identifies the practices necessary to bring a system into statistical control, allows us to determine if the system remains in statistical control, and suggests a course of corrective action when the system has fallen out of statistical control. 

The objective of sediment and water sampling is to obtain reliable information about the behavior of agrochemicals applied to paddy fields. Errors or variability of results can occur randomly or be due to bias. The two major sources of variability are sediment body or water body variability and measurement variability . For the former, a statistical approach is required the latter can be divided into sampling variability, handling, shipping and preparation variability, subsampling variability, laboratory analysis variability, and between-batch variability. ... 

Cochrane Library. The Cochrane Library [44] includes The Cochrane Database of Systematic Reviews, a collection of regularly updated, systematic reviews of the effects of health care. It is maintained by contributors to the Cochrane Collaboration. Cochrane reviews are reviews mainly of randomized controlled trials. To minimize bias, evidence is included or excluded on the basis of explicit quality criteria. Data are often combined statistically, with meta-analysis, to increase the power of the findings of numerous studies, each too small to produce reliable results individually. Database of Abstracts of Reviews of Effectiveness is also included. It consists of critical assessments and structured abstracts of good systematic reviews published elsewhere. The Cochrane Controlled Trials Register with bibliographic information on controlled trials and other sources of information on the science of reviewing research and evidence-based health care are part of the Cochrane Library. It is commercially available on CD-ROM or the Internet. 

The behavior of the detection algorithm is illustrated by adding a bias to some of the measurements. Curves A, B, C, and D of Fig. 3 illustrate the absolute values of the innovation sequences, showing the simulated error at different times and for different measurements. These errors can be easily recognized in curve E when the chi-square test is applied to the whole innovation vector (n = 4 and a = 0.01). Finally, curves F,G,H, and I display the ratio between the critical value of the test statistic, r, and the chi-value that arises from the source when the variance of the ith innovation (suspected to be at fault) has been substantially increased. This ratio, which is approximately equal to 1 under no-fault conditions, rises sharply when the discarded innovation is the one at fault. 

In some textbooks, a confidence interval is described as the interval within which there is a certain probability of finding the true value of the estimated quantity. Does the term true used in this sense indicate the statistical population value (e.g., p if one is estimating a mean) or the bias-free value (e.g., 6.21% iron in a mineral) Could these two interpretations of true value be a source of misunderstanding in conversations between a statistician and a geologist ... 

Human data and their relevance have to be assessed carefully on a case-by-case basis due to limitations of the techniques available. In particular, attention should be paid to the adequacy of the exposure information, confounding factors, and to sources of bias in the smdy design. The statistical power of the test may also be considered. 

In terms of the origin of ecstasy seizures reported ( mentioned ) by Member States, more than a third of the reporting countries (35%) continue to mention the Netherlands as the main source country (2003-05 period), followed by Belgium (9%). Europe as a whole accounts for 81 per cent of such mentions. There may be a statistical bias, however, as 60 per cent of the coun-... 

The typical user of an SRM performs a measurement of the certified property using the apparatus in his laboratory. He compares his result with the certificate value and, if his measurement fails to reproduce the certified value within acceptable limits of precision he investigates his measurement system to remove sources of bias. There are numerous examples of measurement systems in which the level of agreement among measured results by employing SRM s in this way, and the statistical assessment of the performance of measurement systems employing SRM s is a well-established science (3). 

Several types of bias are common in analytical methodology, including laboratory bias and method bias. Laboratory bias can occur in specific laboratories, due to an uncalibrated balance or contaminated water supply, for example. This source of bias is discovered when results of interlaboratory studies are compared and statistically evaluated. Method bias is not readily distinguishable between laboratories following a standard procedure, but can be identified when reference materials are used to compare the accuracy of different methods. The use of interlaboratory studies and reference materials allows experimentalists to evaluate the accuracy of their analysis. 

These analyses require care and ingenuity, for there are many alternative measures (many ways of correcting for the effects of body size, for example), many alternative statistics and sources of bias to be avoided, such as the effects of phylogenetic relationships between the species used. Analyses have gradually become more sophisticated in all these respects, details of which are far beyond the scope of this short paper. The principal outcome of this work is easy to summarize, however the results of the more recent studies favour the MI hypothesis over the principal competing hypotheses corresponding to physical or technical factors (Barton Dunbar 1997, Dunbar 1998) (see Fig. 1). 

One should also invoke Fermi statistics. A typical tunnel curve is shown in Fig. 12 for SET model with D = 14 a.u., a = 1 a.u., the work function of electrodes W = 0.4 a.u., the Fermi energy Ee = 0.2 a.u., and the polarizability a = 200 a.u. (of Na atom). The potential drops near the interface of the source-drain electrodes, as it should for the ballistic regime. The tunnel curve has a single shallow well at a small bias voltage. When the latter increases, the well becomes deeper, and the dot is attracted to the inter-electrode gap center... 

Aspergillus nidulans (biA-1 sorA-2) germination time and hyphae growth rate as a function of NMR and NMR T2 relaxation times of cellulose (c), sorbose (s), and orange serum broth solids with different s c ratios at 25°C. (Source From Brown, D. and Rothery, P. Models in Biology-Mathematics, Statistics and Computing, Wiley, Chichester, UK, 1993. With permission.)... 

A bias is added to the reactor conversion measurement with a magnitude of about 5 % of the current reactor conversion (sensor 2). Immediately after the bias change is introduced to the sensor, both the univariate chart for reactor conversion and the multivariate and SPE charts (Figure 8.7) indicate an abnormality. A CVSS model is developed to generate the residuals for sensor audit. The KBS automatically begins the sensor validation routine to determine the source cause of the inflated and SPE statistics. Figure 8.8 shows that the residuals mean for sensors 1 (initiator concentration), 2 (reactor conversion), and 4 (polydispersity) have exceeded... 

If exposure misclassification occurred in the studies of MeHg, such misclassification would tend to obscure any trae effect. Therefore, statistically significant dose-response associations are likely to reflect tme dose-response relationships, assuming that other sources of bias are adequately addressed. 

Regression to the mean. A statistical phenomenon and an important (and often unrecognized) source of bias in uncontrolled studies which may be explained by example. If a large group of stable individuals chosen at random have their systolic blood pressure measured at two different times, then the mean of the blood pressure on the two occasions may well be similar and so may the variances. Because of variability within individuals, however, the correlation between repeat measures will not be perfect. The only way in which all these three statistical results (regarding mean, variance and correlation) can be satisfied will be if on average there is an increase for... 

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