Sentence examples for setting bias from inspiring English sources

There may also have been a setting bias for the time students spent working on the cases because the cases were presented at the end of regular seminars.

This can be hampered by measurement biases such as the extent of information given to respondents (information bias) or the starting value of a bid in CVM (setting bias) [ 10, 21].

Considering a possible setting bias, it has to be mentioned that data were collected during a QC session where GPs were asked to participate both in a clinical trial on dementia (the WIDA-trial) and – independently – in this survey [ 19].

The results of our simulation study indicated that in a realistic setting, bias in estimating the difference in S and C between two index tests due to the use of an imperfect reference standard can be relatively limited (Additional file 1).

This calls for efforts by stakeholders in polytechnic education in setting biased policy interventions to stimulate entrepreneurship among female students.

It is an unfortunate side-effect of the underlying neural network caused by the training set bias, not intended behaviour.

The issues include dealing with test set bias caused by, for example, inappropriate normalization procedure, devising appropriate benchmarking criteria and formulating statistically robust feature-selection techniques.

The founder subsequently apologized for what he described as an "unfortunate side-effect of the underlying neural network caused by the training set bias" — and the offending filter was renamed, while the team worked on a fix.

The unusually high internal consistency in rural India and Nigeria (Cronbach's alpha, 0.87) may suggest a problem with response set bias in those sites.

All questions used a 10 point visual analogue rating format scale: 2. To avoid response set bias, the questions in the Nursing Home Administrator Job Satisfaction Questionnaire were presented in a random order, and did not include domain headings.

An obvious weakness of this approach is the effect of data set bias during classification, which is a result of the tendency for highly studied proteins to be overrepresented.