bias

n.

1. A line going diagonally across the grain of fabric: Cut the cloth on the bias.
2. 1. A preference or an inclination, especially one that inhibits impartial judgment.
   2. An unfair act or policy stemming from prejudice.
3. A statistical sampling or testing error caused by systematically favoring some outcomes over others.
4. Sports.
   1. A weight or irregularity in a ball that causes it to swerve, as in lawn bowling.
   2. The tendency of such a ball to swerve.
5. The fixed voltage applied to an electrode.

adj.
Slanting or diagonal; oblique: a bias fold.

tr.v., -ased, or -assed, -as·ing, or -as·sing, -as·es, or -as·ses.

1. To influence in a particular, typically unfair direction; prejudice.
2. To apply a small voltage to (a grid).

[French biais, slant, from Provençal, perhaps ultimately from Greek epikarsios, slanted.]

SYNONYMS   bias, color, jaundice, prejudice, warp. These verbs mean to influence unfavorably or detrimentally: His experiences biased his outlook. Your misbehavior has colored my opinion of you. Dishonest leaders have jaundiced her view of politics. Lying has prejudiced the public against them. Bitterness has warped your judgment. See also synonyms at incline, predilection.

In general: outlook or point of view.

Consumer research: systematic error that comes about because the wording of a questionnaire or the attitude of the interviewer or leader appears to encourage one answer over other possible answers.

Federal Reserve's view concerning interest rates. A bias toward higher rates means the Federal Open Market Committee the Fed's rate-setting committee, may raise the federal funds target rate at its next meeting.

noun

1. An inclination for or against that inhibits impartial judgment: one-sidedness, partiality, partisanship, prejudice, prepossession, tendentiousness. See affect/ineffectiveness, like/dislike, straight/bent.
2. An inclination to something: bent, cast, disposition, leaning, partiality, penchant, predilection, predisposition, proclivity, proneness, propensity, squint, tendency, trend, turn. See approach/retreat, like/dislike.

adjective

Angled at a slant: beveled, biased, diagonal, oblique, slanted, slanting,, straight/bent.

verb

1. To cause to have a prejudiced view: jaundice, prejudice, prepossess, warp. See affect/ineffectiveness, straight/bent.
2. To direct (material) to the interests of a particular group: skew, slant. Informal angle. See straight/bent.

n

Definition: belief in one way; partiality
Antonyms: fairness, impartiality, justness

v

Definition: cause to favor
Antonyms: be fair, be impartial, be just

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n

1. a prejudiced or subjective attitude. n 2. in statistics, the systematic distortion of a statistic caused by a particular sampling process.

The validity or capacity of scientific or medical studies to generalize is often put at risk through the introduction of bias. Such bias results from systematic, nonrandom effects that, even in a large study, produce an incorrect answer or result by weakening, distorting, or spuriously creating a relation between a risk factor or intervention and the observed outcome. It might be caused by a reference population different from the intended group. Therefore, bias has the potential to jeopardize study validity. Researchers must recognize this potential, and reduce its effects through study design, analysis, and interpretation. Controlled laboratory experiments and randomized clinical trials are less prone to bias than are observational studies such as cohort or case-control studies, but this protection is only available for a limited set of conclusions, and bias must be addressed in all studies.

There are many types of bias, which can be intentional or unintentional, and events or features that bias one study may have no biasing effect on another. Biases can result from selection effects (e.g., the sampling plan leaves out a sub-group, over represents a subgroup, or has more complete follow-up for a subgroup [the healthy worker effect]); differential measurement (e.g., cancer cases provide a more accurate family history or exposure history than do controls), measurement error (e.g., the recorded and actual exposures to cigarette smoke differ), and a host of other factors.

Bias is a loaded term in that not all bias is bad. For example, in small studies use of a statistically biased estimate (an estimate that on average does not equal the population valve) can have substantially lower variance than the unbiased estimate and thus be preferred. Regression techniques rely on this trade-off between variance and bias to decide on the valve of entering additional explanatory variables.

Additional examples of bias include the following:

• Conscious selection: A randomized clinical trial requires participants to have the disease of interest, but not be too ill. The treatment comparison is internally valid, but generalizing findings to all diseased individuals may introduce a bias.
• Regression dilution: Reducing elevated blood pressure is known to reduce the risk of a myocardial infarction. However, blood pressure is measured with error, and regression dilution produces an attenuated (biased) relation between the intervention and risk.
• Drop out bias: For an interesting example of bias consider a study of the effects of coaching on SAT scores, reporting that students completing the coaching program averaged a fifty-point-higher score on their next SAT exam than those who dropped out. This result is unbiased in comparing completers with noncompleters; however, the result is positively biased in assessing the effect of a coaching program on all who start the program, irrespective of whether they complete it.

Other types of bias typically encountered in epidemiologic research, particularly those employing observational designs, include recall and observer bias. Recall bias arises if one group systematically over- or underreports information about an exposure or risk factor in comparison to the other group. Observer bias occurs if one group is systematically "observed" and reported to behave in a manner that is different from the other group.

Careful design and conduct of studies and careful interpretation of results are necessary to reduce or eliminate bias. Minimizing bias in design and conduct is preferable to relying on post hoc statistical "cures" such as covariance of adjustment and causal modeling. These powerful techniques are absolutely necessary in analyzing observational studies and can be used to "mop up" some bias in designed experiments, but their effectiveness depends on model validity and expert tuning to the specific study.

(SEE ALSOCase-Control Study; Causality, Causes, and Causal Inference; Cohort Studies; Observational Studies)

Bibliography

Last, J. M. (1998). A Dictionary of Epidemiology, 2nd edition. New York: Oxford University Press.

Rothman, K. J., and Greenland, S. (1998). Modern Epidemiology, 2nd edition. Philadelphia, PA: Lippincott-Raven.

— GERMAINE M. BUCK; THOMAS A. LOUIS

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1. In research, the distortion of data or findings by the research method employed or by the researcher's suppositions. Bias results in a loss of accuracy, reliability, and validity of the research.

2. In statistics, a difference between the hypothetical ‘true value’ of a variable in a population and that obtained in a particular sample.

This entry contains information applicable to United States law only.

A predisposition or a preconceived opinion that prevents a person from impartially evaluating facts that have been presented for determination; a prejudice.

A judge who demonstrates bias in a hearing over which he or she presides has a mental attitude toward a party to the litigation that hinders the judge from supervising fairly the course of the trial, thereby depriving the party of the right to a fair trial. A judge may recuse himself or herself to avoid the appearance of bias.

If, during the voir dire, a prospective juror indicates bias toward either party in a lawsuit, the juror can be successfully challenged for cause and denied a seat on the jury.

Any systematic error in the design, conduct or analysis of a study which results in estimates which depart from true values. An unbiased study is free from systematic error. Many types of bias have been named, but three general types can be identified, selection bias, information bias and confounding. Selection bias is a systematic error in a study caused by the individuals selected into the study being different from the entire target population in an important way. See also berkson's bias. Information bias is a systematic error in a study caused by errors in the data which are collected in the study, or in the analysis of the data.

IN BRIEF: A like or dislike of something that unfairly influences an opinion.

Bias has to be taught. — Barbara Bush

This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)

Dansk (Danish)
n. - bias, skævvridning, skævhed
v. tr. - påvirke til forudindtagethed, gøre forudindtaget
adj. - forudindtaget, skævvride
adv. - skrå, skråtskåret

idioms:

• bias cut    skråtskåret
• on the bias    skråt, på skrå

Nederlands (Dutch)
vooringenomenheid, neiging (richting), vertekening (statistiek), diagonaal (stof), voltage/magnetisch veld (elektronica), beïnvloeden, doen afwijken, schuin (knippen)

Français (French)
n. - tendance, inclination, penchant, préjugé, parti pris, prévention, (Jur) distorsion, (Cout) biais, (Sport) poids placé à l'intérieur d'une boule, déviation
v. tr. - influencer en faveur de/contre, prévenir en faveur de/contre
adj. - partial, subjectif, déformé, tendancieux
adv. - en diagonale, obliquement

idioms:

• bias cut    coupure en biais
• on the bias    dans le biais

Deutsch (German)
n. - Neigung, Voreingenommenheit, (Statistik) Systematik, (Tech.) konstante Stromzufuhr
v. - auf eine Seite lenken, beeinflussen, (übertr.) hinlenken
adj. - diagonal, schräg
adv. - diagonal, schräg

idioms:

• bias cut    schräg geschnitten
• on the bias    schräg zum Fadenschnitt

Ελληνική (Greek)
n. - προκατάληψη, μεροληψία, ιδιαίτερη συμπάθεια, λοξή ραφή, λοξότητα, διαγώνιος, φάλτσο (σφαίρας κ.λπ.), (Η/Υ) παραμόρφωση, (τεχν.) πόλωση
adj. - λοξός, διαγώνιος
adv. - λοξά, διαγωνίως

idioms:

• bias cut    λοξή κοπή
• on the bias    διαγώνια, λοξά

Italiano (Italian)
pregiudizio, intenzione, preconcetto, prevenzione

idioms:

• on the bias    diagonalmente

Português (Portuguese)
n. - linha (f) oblíqua, tendência (f), propensão (f)
adj. - diagonal
adv. - diagonalmente

idioms:

• bias cut    corte (m) em viés
• on the bias    diagonalmente

Русский (Russian)
предвзятое отношение, предрассудок, пристрастие, предубеждение, резать по косой материала

idioms:

• bias cut    скроенный по косой линии
• on the bias    по диагонали, диагональный

Español (Spanish)
n. - tendencia, prejuicio, predisposición, sesgo, tensión
v. tr. - torcer, influir, predisponer, inclinar
adj. - sesgado, parcial, diagonal
adv. - tendenciosamente, prejuiciosamente, parcialmente, oblicuamente, en diagonal

idioms:

• bias cut    al bies, en diagonal
• on the bias    al sesgo, al bies

Svenska (Swedish)
n. - förutfattad mening, fördom
adj. - fördomsfull, partisk
adv. - partiskt

中文（简体）(Chinese (Simplified))
偏见, 成见, 倾向, 趋势, 偏心, 偏爱, 使存偏见, 斜的, 斜纹的

idioms:

• bias cut    斜切布
• on the bias    偏斜地, 沿着织物的对角方向

中文（繁體）(Chinese (Traditional))
n. - 偏見, 成見, 傾向, 趨勢, 偏心, 偏愛
v. tr. - 使存偏見
adj. - 斜的, 斜紋的

idioms:

• bias cut    斜切布
• on the bias    偏斜地, 沿著織物的對角方向

한국어 (Korean)
n. - 사선, 바이어스, 선입관
v. tr. - 편견을 갖게 하다, 한쪽으로 치우치게 하다
adj. - 비스듬히 자른, 바이어스의
adv. - 대각선으로, 비스듬히

日本語 (Japanese)
n. - 心理的傾向, 先入観, 斜線, バイアス, 傾向, えこひいき
adj. - 斜めの
v. - 偏らせる, 偏見を抱かせる

idioms:

• bias cut    バイアスカット
• on the bias    斜めに

العربيه (Arabic)
‏(الاسم) تحيز, انحراف, ميل (صفه) مائل, منحرف (ظرف) مائلا‏

עברית (Hebrew)
n. - ‮נטייה, דעה קדומה, נטאי, סטייה (סטטיסטיקה), מתח חשמלי קבוע‬
v. tr. - ‮שיחד, הטה דעה, השפיע‬
adj. - ‮נוטה בדעותיו לצד אחד‬

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bias tires