bias


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bias

 [bi´as]
1. (in a measurement process) systematic error.
2. any influence or action at any stage of a study that systematically distorts the findings.
3. (of a statistical estimator) the difference between the expected value of the estimator and the true parameter value.

bi·as

(bī'-as),
1. Systematic discrepancy between a measurement and the true value; may be constant or proportionate and may adversely affect test results.
2. Any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that differ systematically from the truth; deviation of results or inferences from the truth, or processes leading to deviation.
[Fr. biais, obliquity, perh. fr. L. bifax, two-faced]

There is no imputation of prejudice, partisanship, or other subjective or emotional factor such as an investigator's desire to achieve a particular outcome. More than 100 varieties of bias have been described, but all fall into a small number of classes: 1. Systematic one-sided variation of measurements from the true value. SYN systematic error, instrumental error 2. Variation of statistical summary measures (means, rates, measures of association) from their true values as a result of systematic variation of measurements, other flaws in data collection, or flaws in study design or analysis. 3. Deviation of inferences from the truth as a result of flaws in study design, data collection, or the analysis or interpretation of results. 4. A tendency of procedures in study design, data collection, analysis, interpretation, review or publication, to yield results or conclusions that depart from the truth. 5. Prejudice leading to the conscious or subconscious selection of study procedures that depart from the truth in a particular direction, or to one-sidedness in interpretation of results. This last form of bias can arise as a result of shoddy scientific methods or deliberate misrepresentation of the truth by investigators.

bias

Epidemiology Deviation of results or inferences from the truth, or processes leading to such systematic deviation; any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically incorrect

bi·as

(bī'ăs)
1. Systematic discrepancy between a measurement and the true value; may be constant or proportionate and may adversely affect test results.
2. Any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that differ systematically from the truth; deviation of results or inferences from the truth, or processes leading to deviation.
[Fr. biais, obliquity, perh. fr. L. bifax, two-faced]

bi·as

(bī'ăs)
1. Systematic discrepancy between a measurement and the true value; may be constant or proportionate and may adversely affect test results.
2. Any trend in the collection, analysis, interpretation, publication, or review, which can lead to conclusions that differ systematically from the truth; deviation of results or inferences from the truth, or processes leading to deviation.
[Fr. biais, obliquity, perh. fr. L. bifax, two-faced]
References in periodicals archive ?
If these prevent you from providing a service, ensure that you refer patients to other appropriate providers 13.4 Respect colleagues' skills and contributions and do not discriminate CORU Standards of Proficiency for Optometrists (5) and Dispensing Opticians' 1.8 (Optometrists) 1.10 (Dispensing Opticians) Recognise the importance of practising in a non-discriminatory, culturally sensitive way and acknowledge and respect the differences in beliefs and cultural practices of individuals or groups Table 2 Other forms of bias potentially influencing healthcare practice Confirmation bias A tendency to seek and establish diagnostic evidence for an existing diagnosis, while ignoring information that refutes this diagnosis.
"This is an instance where bias may offer cost savings.
The anchoring bias leads you to evaluate your choices according to some baseline.
Results showed that [Y.sub.2][O.sub.3] dense films were obtainable under the deposition conditions: low [Y.sub.2][O.sub.3] concentration (0.5 wt%), high bias frequency (higher than 1kHz), and low bias voltage (0.5 V).
The real question is, "How do we deal with biases that may compromise our determination to rationally choose new directors?" As in most cases of potential bias, the first step is to help directors be self-aware.
To be included in the study participants were required to be moderate social drinkers, defined as reporting average weekly consumption of greater than ten units of alcohol (ten units per week or less is defined as light drinking by Field, Mogg, Zetteler, and Bradley (2004)), but less than 25 units per week (25 or more is defined as heavy social drinking by Townshend and Duka (2001)) as it has been found that non-drinkers and very light occasional drinkers do not exhibit attentional bias to alcohol cues (Field, Mogg, Zetteler, Bradley, 2004; Townshend & Duka, 2001).
The process of learning racial bias is a lot like learning a new language (e.g., a child raised bilingual vs.
There are two main ways that bias shows up in training data: either the data you collect is unrepresentative of reality, or it reflects existing prejudices.
Bias may damage credibility, just as untrustworthiness does., but that does not mean that bias and untrustworthiness always have the same consequences.
But as Chris Dowsett, head of marketing analytics and decision science at Instagram, stated in Towards Data Science (bit.ly/2I9jGik), "The fact that humans are both creators and users of data means there is an opportunity for bias to creep into the data life-cycle."
Considering that a certain volume of the interferent was added in a pooled sample, measured results were corrected according to dilution factor in order to eliminate bias as a result of sample dilution.