favoring one person or side over another; "a biased account of the trial"; "a decision that was partial to the defendant"
If someone is biased, they prefer one group of people to another, and behave unfairly as a result. You can also say that a process or system is biased. He seemed a bit biased against women in my opinion The judge was biased. = prejudiced
In the game of crown green bowls: a weight added to one side of a bowl so that as it rolls, it will follow a curved rather than a straight path; the oblique line followed by such a bowl; the lopsided shape or structure of such a bowl
The degree of correspondence between the mean forecast (<f>) and the mean observation (<x>) This type of bias is also known as overall bias, systematic bias, or unconditional bias The mean error is a measure of the overall forecast bias for continuous and probabilistic forecasts A measure of bias for categorical forecasts is equal to the total number of event forecasts (hits + false alarms) divided by the total number of observed events With respect to the 2x2 verification problem example outlined in the definition of contingency table, bias= (A+B)/(A+C)
A tendency to misrepresent The term bias is used in statistics to refer to how far the average statistic lies from the parameter it is estimating, that is, the error that arises when estimating a quantity Errors from chance will cancel each other out in the long run, those from bias will not
Bias occurs when problems in study design lead to effects that are not related to the variables being studied An example is selection bias, which occurs when study subjects are chosen in a way that can misleadingly increase or decrease the strength of an association Choosing experimental and control group subjects from different populations would result in a selection bias
the systematic tendency of any factors associated with the design, conduct, analysis and evaluation of the results of a clinical trial to make the estimate of a treatment effect deviate from its true value Bias introduced through deviations in conduct is referred to as 'operational' bias The other sources of bias listed above are referred to as 'statistical'
Extent to which, over repeated samples, the mean of the sampling distribution differs from the true mean Bias is generally hard to quantify, but is likely to increase if the sampling frame is deficient and/or the response rate is low
In a CCD camera system, the minimum intensity required for each exposure (equivalent to performing a zero-second exposure with the shutter closed) Without adding any light, the bias allows charge to be read out on the CCD while raising the intensity level high enough to ensure that the camera does not deliver a negative number to the A/D converter (The A/D converter only works in the set of positive numbers and has no instructions for processing negative numbers ) The bias, which is not user selectable, is set at the factory and remains stable over the lifetime of the camera system See CCD readout
Deviation of results or inferences from the truth It can occur at any stage of a study from its design to the application of the information obtained from the study Bias can be reduced by proper study design and execution and not by increasing sample size Almost all studies have bias, but to varying degrees The key question is whether or not the results could be due in large part to bias, thus making the conclusions invalid Observational study designs are inherently more susceptible to bias than are experimental study designs The main categories are
(1) a systematic tendency toward a lack of objectivity, fairness, or impartiality on the part of the assessor or evaluator, often based on personal preferences and inclinations (2) systematic error in the assessment instrument and procedures, or in the interpretation and evaluation process See Contamination, Differential Functioning, Error of Measurement, Fairness, Rater Effect
Bias is a tendency to prefer one person or thing to another, and to favour that person or thing. Bias against women permeates every level of the judicial system There were fierce attacks on the BBC for alleged political bias. = prejudice
This is any factor which might change the results of a study from what they would have been if that factor were NOT present The direction of bias may be unpredictable For example, giving a team a ten point advantage might seem to give that side an advantage but some teams actually play much better when they have to come from behind! The validity of a study is integrally related to the likelihood that the results have been biased by factors extraneous to the study design
A non-chance event arising from faults in study design or measurement or data collection Bias may prejudice results in that traditional statistical analysis may be precluded or unreliable Bias may be introduced into a study by many factors including subject selection, follow-up, study factor choice, unmasked data collection, temporal trends in disease, co-management of disease if not concurrent in time, ecological fallacy, retrieval methods, play of chance, publication choice or prejudice of investigators
(refers to statistical bias): Inaccurate representation that produces systematic error in a research finding Bias may result in overestimating or underestimating certain characteristics of the population It may result from incomplete information or invalid collection methods, and may be intentional or unintentional
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 different from the truth
A systematic tendency of a sample to misrepresent the population Biases may be caused by improper representation of the population in the sample, interviewing techniques, wording of questions, data entry, etc
In a sampling context, the difference between the conceptual weighted average value of an estimator over all possible samples and the true value of the quantity being estimated An estimator is said to be unbiased if that difference is zero The "systematic or persistent distortion of a measurement process which deprives the result of representativeness (i e , the expected sample measurement is different than the sample's true value) A data quality indicator" (QAMS 1993, 3)
The existence of a selective factor that will operate to determine the content of the sample in a particular way Generally, bias is an effect which deprives a statistical result of representatives by systematically distorting it, as compared to a random error which may distort on any one occasion, but which tends to balance on the average An example of the possibility of introducing a bias in sample results is to take a sample from a purchase journal where every tenth line is a utility bill A systematic sample with a sampling interval of (10) or some multiple of (10) will include either all utility bills or none Another example would be the selection for testing only those items which are simple to test or easy to locate
A characteristic of a test that could reduce the chances for identifiable sub-populations to receive scores that accurately reflect their abilities to respond to the skill being measured Common sources of bias may be related to language, cultural, or gender differences Example: A mathematics word problem that contains difficult language may be biased against English language learners Inadequate performance may not be due to a lack of mathematical ability, but rather a lack of English language skills
a false association that results from to the failure to account for some skewing or influencing factor, or a tendency for the observed results to deviate from the "true" results Bias distorts results in a particular direction For example, if an investigator in a clinical trial believes the drug under study to be effective and knows which participants are receiving the drug, bias may influence his/her observations in favor of positive results
In feedforward and some other neural networks, each hidden unit and each output unit is connected via a trainable weight to a unit (the bias unit) that always has an activation level of -1 This has the effect of giving each hidden or output a trainable threshold, equal to the value of the weight from the bias unit to the unit
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