In social psychology, a positive stereotype refers to a subjectively favourable belief held about a social group. Common examples of positive stereotypes are Asians with better math ability, African Americans with greater athletic ability, and women with being warmer and more communal.

Gender roles are influenced by the media, family, environment, and society. A child’s understanding of gender roles impacts how they socialize with their peers and form relationships.

Because stereotypes simplify and justify social reality, they have potentially powerful effects on how people perceive and treat one another. As a result, stereotypes can lead to discrimination in labor markets and other domains.

A stereotype is a mistaken idea or belief many people have about a thing or group that is based upon how they look on the outside, which may be untrue or only partly true. Stereotyping people is a type of prejudice because what is on the outside is a small part of who a person is.

For example, stereotype threat has been shown to disrupt working memory and executive function, increase arousal, increase self-consciousness about one’s performance, and cause individuals to try to suppress negative thoughts as well as negative emotions such as anxiety.

Essentially, stereotyping in advertising is the use of caricatures, be they based on truth or perception, to portray an idea in a short amount of time.

a mindless prejudice against the unfamiliar bias implies an unreasoned and unfair distortion of judgment in favor of or against a person or thing. a strong bias toward the plaintiff. Verb. incline, bias, dispose, predispose mean to influence one to have or take an attitude toward something.

A coin that has two different sides for two different results,irrespctive of how many trials you do. Outcomes are not equally likely, they might be equal or might not be equal, it is biased coin.

In statistics, the bias (or bias function) of an estimator is the difference between this estimator’s expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased.

Statistical bias is a feature of a statistical technique or of its results whereby the expected value of the results differs from the true underlying quantitative parameter being estimated.

Bias is taken to mean interference in the outcomes of research by predetermined ideas, prejudice or influence in a certain direction. Data can be biased but so can the people who analyse the data. When data is biased, we mean that the sample is not representative of the entire population.

Question order bias, or “order effects bias”, is a type of response bias where a respondent may react differently to questions based on the order in which questions appear in a survey or interview.

Priming is an extension of agenda setting, the process by which new organizations increase salience of particular ideas, making them more likely to be recalled later.

In research—particularly in psychology—the term demand characteristic refers to an experimental artifact where participants form an interpretation of the experiment’s purpose and subconsciously change their behavior to fit that interpretation.

There are several forms of reactivity. The Hawthorne effect occurs when research study participants know they are being studied and alter their performance because of the attention they receive from the experimenters.

Research impact is the effect research has beyond academia. The York Research Impact Statement (PDF , 286kb) describes research impact as “… when the knowledge generated by our research contributes to, benefits and influences society, culture, our environment and the economy”.

Participation bias or non-response bias is a phenomenon in which the results of elections, studies, polls, etc. become non-representative because the participants disproportionately possess certain traits which affect the outcome.

In research, the observer bias is a form of detection bias originating at a study’s stage of observing or recording information. Different observers may assess subjective criteria differently, and cognitive biases (including preconceptions and assumptions) can affect how a subject is assessed.

Participants will sometimes second-guess what the researcher is after, or change their answers or behaviors in different ways, depending on the experiment or environment [1]. This is called participant bias, or response bias, and it can have a huge impact on research findings.

Explanation. Self-selection makes determination of causation more difficult. Self-selection bias causes problems for research about programs or products. In particular, self-selection affects evaluation of whether or not a given program has some effect, and complicates interpretation of market research.

Selection bias is the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed. It is sometimes referred to as the selection effect.

In social science research, social-desirability bias is a type of response bias that is the tendency of survey respondents to answer questions in a manner that will be viewed favorably by others. It can take the form of over-reporting “good behavior” or under-reporting “bad”, or undesirable behavior.

In context of (clinical or biological) psychology The novelty effect is the tendency for an individual to have the strongest stress response the first time that individual is faced with a potentially threatening experience. Over time, as the novelty wears off, the stress response decreases.

Priming is a phenomenon whereby exposure to one stimulus influences a response to a subsequent stimulus, without conscious guidance or intention. Priming can be perceptual, associative, repetitive, positive, negative, affective, semantic, or conceptual.

A sample drawn and recorded by a method which is free from bias. This implies not only freedom from bias in the method of selection, e.g. random sampling, but freedom from any bias of procedure, e.g. wrong definition, non-response, design of questions, interviewer bias, etc.

An estimator can be unbiased for all n but inconsistent if the variance doesn’t go to zero, and it can be consistent but biased for all n if the bias for each n is nonzero, but going to zero.

An efficient estimator is an estimator that estimates the quantity of interest in some “best possible” manner. The notion of “best possible” relies upon the choice of a particular loss function — the function which quantifies the relative degree of undesirability of estimation errors of different magnitudes.

If var θ ( U ) ≤ var θ ( V ) for all θ ∈ Θ then is a uniformly better estimator than . If is uniformly better than every other unbiased estimator of , then is a Uniformly Minimum Variance Unbiased Estimator ( UMVUE ) of .