Attributional bias example

Figure 17a shows, as an example, the plateau data-point histograms of T3 at three bias voltages. Each histogram, constructed from more than 1,000 individual traces, reveals a distinct maximum. The peak positions from individual experiments are very reproducible for low bias voltages Vbias < 0-30 V. The broad asymmetric tail region toward higher conductance values is attributed to contributions from... 

Recently, and for the first time, it has been shown that high levels of diastereocontrol may be realized in the rhodium(l)-catalyzed hydroformylation of acychc alkenes. In one example, acetals 10 afford aldehydes 11 with superior diastereoselectivity (Scheme 5.4) [4]. This result was attributed to a strong conformational bias in the substrate, as shown. Evidence for this conformational bias was secured by 2D-NOESY NMR experiments and MM3 force field calculations. When the experiment was repeated with R=H, the diastereoselectivity was lost, lending further support to the model. 

It is seen from Fig. 11.1 that, beyond any bias from the personal choice of the examples, the isoprenoids show the largest skeletal variety in all ecosystems. The absolute maximum is observed for the isoprenoids from the Indo-Pacific (dark green ribbon, sixth from the right), which represents a triunqjh of the natural product diversity of Indo-Pacific coral reefs. To its left, the pale violet ribbon for the Caribbeans shows a similar trend, although with lower values attributable to the more restricted area with less extensive and varied coral reefs. 

Detection bias occurs in convenience-cohort studies when any measure of substance exposure is correlated with differences in medical scrutiny. For example, the positive relationship between reser-pine (a blood pressure medication) and breast cancer might be attributable to the fact that women under treatment for high blood pressure are more likely to have breast exams, which detect otherwise silent breast cancers (Feinstein 1988, 1261). The same might be true of the relationship between alcohol intake and breast cancer, because alcohol could be a surrogate for income and more frequent breast cancer screening and mammography (Feinstein 1988, 1261). 

Examination of product control charts is most useful in trying to distinguish between process-related or non-process-related causes.Trend analysis of key production parameters and attributes could assist in localizing a possible cause of the OOS. For example, if the potency of the product has been trending higher than usual for the last few batches produced (and the OOS resulted from an upper limit failure), this could be indicative of such causations as inaccurate moisture analysis or operator compensation error, error in the batch record, weighing error due to balance or scale bias, change in excipient purity which could impact functional characteristics or failure to maintain and/or calibrate apiece of equipment. 

An intriguing feature of the VSe2 electrodes sensitized with the thiapentaearbo-cyanine was that the photocurrent action spectra were a function of the bias potential applied to the electrode. For example, the maximum conversion efficiency at -0.4 V vs. Ag/AgCl was 1100 nm but shifted to 1080 nm at -1-0.05 V. The origin of these spectral shifts was attributed to sensitizer aggregates formed on the surface that have different conversion efficiencies [92]. 

In principle, the Kramers-Kronig relations can be used to determine whether the impedance spectrum of a given system has been influenced by bias errors caused, for example, by instrumental artifacts or time-dependent phenomena. Although this information is critical to the analysis of impedance data, the Kramers-Kronig relations have not found widespread use in the analysis and interpretation of electrochemical impedance spectroscopy data due to difficulties with their application. The integral relations require data for frequencies ranging from zero to infinity, but the experimental frequency range is necessarily constrained by instrumental limitations or by noise attributable to the instability of the electrode. 

Subjects will drop out of trials for either random (ignorable) reasons or perhaps for a reason attributable to their disease, trial conditions, or other nonignorable factor. Both conditions are important to consider for efficacy trial simulation. In the former case, subjects who drop out (are missing) at random will result in a decrease in total sample size and may affect the study power. In the latter case, nonrandom dropout is considered to be nonignorable in that the reason for dropout is informative to the trial outcome and may bias the results. In the seminal paper by Sheiner (25), an example of nonrandom dropout is presented for an analgesic trial, where those subjects not achieving adequate pain relief were more likely to drop out (i.e., to take rescue medication). 

Was intention-to-treat analysis performed Intention-to-treat analysis means that the results from all subjects randomized in the study were accounted for and attributed to the group to which they were assigned. This strategy minimizes attrition bias and ensures that the known and unknown prognostic factors are kept equally distributed. For example, exclusion of subjects who withdrew early in treatment may bias the... 

Studies of this type have been used by Smit and co-workers to explain the so-called inverse shape selectivity observed in the conversion of long chain w-alkanes over acid zeolites. In such reactions, product distributions are found to depend on the pore structure, particularly for medium-pore zeolites such as ZSM-5. In some cases branched alkanes are favoured over linear alkanes in the products of medium-pore zeolites compared to the reaction selectivities of large-pore zeolites such as zeolite Y. For example, doubly branched isomers are favoured over ZSM-5. This is in contrast with what would be expected from dilfusion rates and is attributed to the enhanced thermodynamic stability of some branched intermediates in the medium-pore zeolites that is predicted by configurational bias GCMC. 

On the matter of correlations between electrode and spectroscopic data, there can be some bias but while electrode potassium values are very slightly higher in one study [145], it is the flame photometer potassium data that are higher in another [148]. The bias in the second example [148] was attributed to faulty electrode calibration. 

Regardless of external validation methodology, sources of bias like the Hawthorne effect (that any change in a standard environment is perceived by individuals in that environment to be positive) are likely to get in the way of objective analysis. When faced with using an ITS, students will by definition work and learn more effectively due to the very novelty of the ITS, rather than particular attributes of the ITS. This problem has bedevilled scientiric attempts to prove that LOGO, for example, is a better way of teaching mathematical reasoning than more traditional approaches. 

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