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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.
Having an idea about what the study results will show before the clinical trial is conducted.
the consistent underestimation or overestimation of a true value, because of preconceived notion of the person sampling the population.
Statistical sampling or testing for errors caused by systematically favouring some factors over others. Bias is understood to limit the overall validity of research. See also Halo effect; Reliability; Validity
Errors in statistical sampling or testing that are by favoring some factors over others. Bias reduces the overall validity of research. See also: Halo effect, Reliability, Validity
Bias is a statistical measure of how accurate an estimator is. Bias measures how different our estimates will be on average from the true population characteristic being estimated. Bias is a theoretical property, and we often choose estimators that have no bias. We can think of bias in the following way: If we were to conduct our survey over and over and our estimates end up being centered around the true population value, then our estimator is unbiased.
A bias is a flaw in either the study design or data analysis that leads to an erroneous result.
In epidemiology, this term does not refer to an opinion or a point of view. Bias is the result of some systematic flaw in the design of a study, the collection of data, or in the analysis of data. Bias is not a chance of occurrence.
( Stat.) The difference between the population mean as sampled and the average of the sample values that would be obtained in an infinitely large number of repetitions of a sampling process. A sampling process involving such a difference is said to be "biased". ( BCFT).
A measurement procedure or estimator is said to be biased if, on the average, it gives an answer that differs from the truth. The bias is the average (expected) difference between the measurement and the truth.
In a statistical context, a systematic error in a test score. In discussing test fairness, bias may refer to construct underrepresentation or construct-irrelevant components of test scores that differentially affect the performance of different groups of test takers. American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (1999). Standards for educational and psychological testing. Washington, DC: American Educational Research Association, p. 172.
Any operation that allows a particular treatment or replication to be favoured or handicapped by some extraneous source of variation. An unwanted property of the sampling procedure. A systematic discrepancy between an estimate of any quantity (from measurements) and its true value.
A tendency â€“ sometimes artificially induced - for an event to happen more frequently than is statistically normal.
A systematic difference between an estimate of and the true value of the parameter.
A consistant and false departure of a statistic from its proper value.
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'
A realist approach to bias depicts this as consisting of any systematic error that obscures correct conclusions about the subject being studied. Typically, such bias may be caused by the researcher, or by procedures adopted for data gathering, including sampling. The concept makes little sense from a relativist standpoint, though provision of a reflexive account of the research process can help in addressing issues of trust that the concept of bias was intended to resolve.
A constant error; any systematic influence-on measures or on statistical results-irrelevant to the purpose of the evaluation.
systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others. Researchers try to avoid bias through randomized controlled double blind trials.
Something that may lead a researcher to wrong conclusions; for example, mistakes or problems in how the study is planned, or how the information is gathered or looked at. If two different interviewers had different styles that caused people with the same thoughts to give different answers, but the answers were all put together in one pool, there would be a bias. It is impossible to conduct completely bias-free research.
This is the tendency of some (poor) study designs systematically to produce results that are better (rarely if ever worse) than those with a robust design. Bias for diagnostic tests works in different ways to bias in trials of treatment .
Systematic variation; the deviation of results or inferences from the truth, or processes leading to such deviation (whether intended or not); an alternative explanation for an apparent treatment effect.
Bias can be defined as the distortion of the estimated effects caused by a systematic difference between the groups being compared.
The amount by which all the samples in the validation set are on average overestimated or underestimated. The bias is equal to the difference between the average reference value and the average NIRS analysis value. where i = 1 ... n samples Example 1: Regarding the term bias Number Reference value NIRS-analysis value Difference 100 101 105 107 110 110 Mean 105 106 bias:1 A systematic error (bias) can be corrected by calculation, but this only makes sense if a significant bias pertains. The limit value for the bias depends on the calibration and should not exceed 0.6 times the SECV. This applies to calibrations with more than 100 samples. A test of the significance of the bias is, for example (see also Schenk et al. 1989) where t = value of the Student-t-statistic for \alpha with degrees of freedom corresponding to the number of the samples in the calibration set, SECV of the calibration, and n = random sample value for the test of the bias
Systematic inaccuracy in data due to characteristics of the process of creating, collecting, processing, or presenting the data.
(a)Systematic error in results caused by the way observations are taken, e.g. leading questions in a questionnaire; different people consistently judge age differently.(b)The difference between the expected value of an estimator and the parameter value it is trying to estimate.
The presence of influence which causes data (or statistics based on the data) not to reflect the true situation.
The degree to which the expected value of an estimator differs from the true parameter value.
Consistent deviation of measured values from the true value, caused by systematic errors in a procedure.
Bias is a consistent error brought about by experimental design favouring one group over another or by the investigator/data recorder favouring one group over another. In the first case it can be prevented by matching the groups, and in the second case by blinding. See also blind study.
A type of error in which a factor skews the data in one direction. Examples include inadequate randomization, a higher likelihood for studies with positive results to be published, and differences in the care provided to the different groups in a study.
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 statistical context, bias is a systematic error in a test score. In discussing test fairness, bias is created by not allowing certain groups into the sample, not designing the test to allow all groups to participate equitably, selecting discriminatory material, testing content that has not been taught, etc. Bias usually favors one group of test takers over another, resulting in discrimination.
The extent to which a measurement, sampling, or analytic method systematically underestimates or overestimates the true value of an attribute. FOR EXAMPLE, words, sentence structure, attitudes, and mannerisms may unfairly influence a respondent's answer to a question. Bias in questionnaire data can stem from a variety of other factors, including choice of words, sentence structure, and the sequence of questions.
Differences among data in which patterns can be detected where the patterns are confounded with (cannot be separated from) the conclusions one wishes to draw). EX: concluding that better education is associated with better oral health may be biased by the confounding of education with income. [See independence among variables, random variation
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.
The systematic distortion of information, by over- or under - representation of certain kinds of persons or information sources, or by data interpretation with pre-determined expectation or desired outcomes, consciously or unconsciously. This distortion can have a number of underlying causes. One example is the use of telephone surveys, which systematically exclude those without telephones, and may result in drawing an inaccurate sample of the population.
A conscious or unconscious tendency to mislabel observations in a nonrandom (systematic) manner.
an unintentional error in a particular direction that may produce misleading or erroneous conclusions
a systematic error encouraging one outcome over others
The presence of patterns or flaws which cause arbitrary tendencies in data, compromising their validity (example: an unbalanced die with a tendency to favor one number).
Human choices or any other factors beside the treatments being tested that affect a study's results. Clinical trials use many methods to avoid bias, because biased results may not be correct.
Any effect at any stage of an investigation tending to produce results that depart systematically from the true values i.e. a systematic error. (See also Random Error)
potential source of error in epidemiology studies. If people with/without the risk factor are more likely to be included in a study, results may not be impartial.
Systematic error that is manifested as a consistent positive or negative deviation from the known or true value. It differs from random error which shows no such deviation.
Bias in testing results in scores that are higher or lower than they would be it the measurement were more reliable and valid. The error caused by bias is systematic rather than random.
A systematic error introduced through some aspect of the study design. It cannot be controlled for in the analysis and efforts must therefore be made to prevent it through good study design and data collection.
A systematic error inherent in a method or caused by some feature of the measurement system.
A term which refers to how far the average statistic lies from the parameter it is estimating, that is, the error which arises when estimating a quantity. It is also referred to as "systematic error". It is the difference between the mean of a model prediction or of a set of measurements and the true value of the quantity being predicted or measured. Errors from chance will cancel each other out in the long run; those from bias will not (statistics). [6
Bias occurs when the assessment process lacks objectivity, fairness, or impartiality in some way. This may disadvantage or discriminate against an individual or group of students. Bias may take the form of flawed assessment tools, design, procedures, analyses or reporting processes. Unbiased assessment is inclusive and works towards equitable outcomes for all learners.
(Syn: systematic error): Deviation of results or inferences from the truth, or processes leading to such deviation. See also Referral Bias, Selection Bias. ( Harm, Therapy)
The difference between the expected value of a statistic and the population value. It is intended to estimate. See EXPECTED VALUE.
There are a number of types of biases which may arise in the context of research or randomised controlled trials: selection bias: systematic differences in comparison groups performance bias: systematic differences in delivery of care between experimental and control groups attrition bias: differences in withdrawals from the trial detection bias: systematic differences in outcome assessment (Gowing, 2001).
Any factor, recognised or not, that alters presentation or availability of data, effects of treatment, or assessment of findings by study participants, researchers, publishers or reviewers themselves.
in general, any factor that distorts the true nature of an event or observation. In clinical investigations, a bias is any systematic factor other than the intervention of interest that affects the magnitude of (i.e., tends to increase or decrease) an observed difference in the outcomes of a treatment group and a control group. Bias diminishes the accuracy (though not necessarily the precision) of an observation. Randomization is a technique used to decrease this form of bias. Bias also refers to a prejudiced or partial viewpoint that would affect someone's interpretation of a problem. Double blinding is a technique used to decrease this type of bias.
A tendency to underestimate or overestimate a population value of interest.
Human choices, beliefs, or any other factors besides those being studied that affect a clinical trial's results. Clinical trials use many methods to avoid bias because biased results may not be accurate.
A deviation of results from the truth. Any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth.
The systematic or persistent distortion of a measurement process, which causes errors in one direction (i.e., the expected sample measurement is different from the sample's true value).
The systematic difference in collected statistical data from the actual characteristic(s) of the target population. Sources of bias are the sample frame, sample selection, respondents and data processing.
Partial judgment on issues relating to the subject of that point of view. Bias is controlled in clinical trials by blinding and randomization.
When a point of view prevents impartial judgment on issues relating to the subject of that point of view. In clinical studies, bias is controlled by blinding and randomization
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.
Systematic over- or under-estimation of a quantity.
A flaw in the study design that could skew the results in favor of a particular conclusion. Researchers try to eliminate bias in clinical trials to get an impartial and scientific result; however, it is very hard to eliminate all bias from a study.
Flaws in the collection, analysis or interpretation of research data that lead to incorrect conclusions.
Deviation of results or inferences from the truth, or processes leading to such deviation. See also Referral Bias, Selection Bias. ( Harm, Therapy) Keyword(s): systematic error
Systematic error; the difference between the mean of all possible estimates given by an estimator and the parameter being estimated
A systematic tendency of a sample to misrepresent the population. Biases may be caused by inadequate sampling of the population, survey or item non-response, interviewing techniques, wording of questions, data entry, etc.
The difference between the expected value of an estimator and the actual value to be estimated.
An error caused by favouring some outcomes more than others.
It is defined as any factor or process that tends to deviate the results or conclusions of a study systematically away from the ‘truth'. This deviation can result in distortion of the effects of an intervention.
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.
Non-random deviation of results from the truth. There are many causes of bias caused by flaws in study design and data collection, analysis, and interpretation. There is no implication that bias is pejorative although it may be.
An aspect of survey design which causes the expected value of an estimate derived from the survey to differ from its true value.
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.
A slanted perspective that prevents researchers from getting true answers to research questions. Clinical trials use several methods to eliminate bias, including randomization, blinding, and the use of strict protocols.
Deviation of results from the truth or mechanisms leading to such deviation (e.g. analysis bias, measurement bias, publication bias, selection bias, withdrawal bias and others). In clinical studies, bias is mainly controlled by blinding and randomisation.
Any difference between the true value and that actually obtained due to all causes other than sampling variability.
A tendency of an estimate to deviate in one direction from a true value.
Someone or something in a study that ignores a confounding variable. This introduces error into the results of a study.
A systematic error which contributes to the difference between a population mean of measurements or test results and an accepted reference value.
the amount we are off from the true value. How wrong we are when we don't get it right.
A situation that occurs in testing when items systematically measure differently for different ethnic, gender, or age groups. Test developers reduce bias by analyzing item data separately for each group, then identifying and discarding items that appear to be biased.
In a sampling sense, a systematic distortion that may arise from many sources, such as a flaw in measurement, method of sample selection, or technique of estimating a parameter.
The persistent positive or negative deviation of the average value from the known or assumed value. It has numerous causes, such as sample preparation, heterogeneity, instrument non-linearity and others. Bias usually refers to a specific analytical instrument.
In a clinical trial, a flaw in the study design or method of collecting or interpreting information. Biases can lead to incorrect conclusions about what the study or trial showed.
is the expected error of an estimator of a random variable. [pg 84-85, 2
The difference between the expected value of the estimate from a probability sample and the true value of the population.