the difference between the expectation of the sample estimator and the true population value, which reduces the representativeness of the estimator by systematically distorting it
inclination towards something; predisposition, partiality, prejudice, preference, predilection
nature has pointed out a mixed kind of life as most suitable to the human race, and secretly admonished them to allow none of these biasses to draw too much.
A leaning of the mind; propensity or prepossession toward an object or view, not leaving the mind indifferent; bent; inclination
A weight on the side of the ball used in the game of bowls, or a tendency imparted to the ball, which turns it from a straight line
In a neural network, bias refers to the constant terms in the model (Note that bias has a different meaning to most data analysts ) Also see precision
a line or cut across a fabric that is not at right angles to a side of the fabric
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