Sentence examples for consequential bias from inspiring English sources

Strickland and Keil (2011) reported a (possibly consequential) bias in event perception: the event-completion effect.

Analyses of the admissions data included in the court filings reveal a far more consequential bias affecting Harvard's admissions decisions: The preference for legacy applicants.

Juries who hold their peers' lives in their hands, police who shoot first and ask questions later, taxi drivers who bypass black customers -- all are instances of quiet but consequential bias that's often belied by what people explicitly state when trying to conform to expected standards of behavior.

The issue of non-participation, and consequential bias that may be introduced, is critically important in case control studies that rely on personal contact to assess environmental experiences and exposures.

Muscle segmentation can be performed semi-automatically with consequential elimination of observer bias.

Our agreement results control for parser bias, and are consequential in that they are on par with state of the art parsing performance for English newswire.

In the more interesting case where bias is epistemically consequential, the prospects for realism are diminished, but may be enhanced by a scientific infrastructure that functions to bring it under scrutiny (by means of, for example, effective peer review, genuine consideration of minority views, etc)., thus facilitating corrective measures where appropriate.

Fluency had no influence on the desire to interact with others of the same race, indicating that this potent cognitive bias is especially consequential in the judgments of out-group members.

We suspect that other PTMs, in addition to protein phosphorylation illustrated in aforementioned hypothetical cases, might also contribute to such biases, for instance, ubiquitination and the consequential proteasomal degradation, which have important consequences for biological conclusions drawn from these investigations.

As humans use machines to inform more and more consequential decisions, it is becoming crucial to understand how bias might occur in algorithmic systems.

This differs from time series cross-sectional data in that the asymptotic properties pertain to N rather than to T. Ignoring the panel structure of the data can be highly consequential statistically (e.g., violations of OLS assumptions that produce bias and inefficiency) and can limit the substantive questions researchers are able to explore.