Sentence examples for chances of erroneous from inspiring English sources

Pilot studies and exploratory analyses that investigate the precision of sampling regimes may reduce the chances of erroneous results and can be used to optimise processing time in larger ecological research programs.

"I've sat in the room with people who argued that the public relations campaign and the extensive activities have increased the chances of erroneous enumeration," said Stephen E. Fienberg, a statistician and social scientist at Carnegie Mellon University.

They are skeptical of analyses with multiple end points or multiple subsets, knowing that the chances of erroneous conclusions increase rapidly once one leaves the context of a focused single hypothesis clinical trial.

Pre-defined standardization rules, drawing rules, automated normalization, and enhanced stereochemistry labeling decrease the chance of erroneous or ambiguous registry.

Although the chance of erroneous associations is probably reduced, the problem remains that the patients being matched may differ in unmeasured ways.

However, such behaviour is highly likely to increase the chance of erroneous medication intake and was for this reason registered as nonadherence.

Both studies show that the chance of erroneous drug use seems to increase for patients who are using more than one pharmacy for their drug purchases and for patients who are, for various reasons, not well informed about the intervention.

Furthermore, since these databases are significantly larger, the chance of erroneous peptide matching is significantly increased and thus warrants manually evaluating each peptide mapping outside pre-defined open reading frame, especially when the peptide is infrequently detected in samples analyzed.

The design evaluates the clinical utility of the trial by (i) demonstrating the information that a Bayesian analysis could provide for a range of possible observed trial results and prior distributions; and (ii) assessing the operating characteristics of the design i.e. the chance of erroneous conclusions from typical decision criteria under a range of underlying true scenarios using simulation.

Unlike Aesop's Boy Who Cried Wolf and Hilaire Belloc's Matilda, the chances of your erroneous, collective screeching landing you in hot water seem to be covered by sheer weight of numbers.

Moreover, the combination of a number of models palliates the effect of extreme predictions by averaging them (regression to the mean), and the chances of obtaining erroneous predicitons with a single model decrease.