Implicit stereotypeAn implicit bias or implicit stereotype is the pre-reflective attribution of particular qualities by an individual to a member of some social out group. Implicit stereotypes are thought to be shaped by experience and based on learned associations between particular qualities and social categories, including race and/or gender. Individuals' perceptions and behaviors can be influenced by the implicit stereotypes they hold, even if they are sometimes unaware they hold such stereotypes.
Implicit-association testThe implicit-association test (IAT) is an assessment intended to detect subconscious associations between mental representations of objects (concepts) in memory. Its best-known application is the assessment of implicit stereotypes held by test subjects, such as associations between particular racial categories and stereotypes about those groups. The test has been applied to a variety of belief associations, such as those involving racial groups, gender, sexuality, age, and religion but also the self-esteem, political views, and predictions of the test taker.
Implicit attitudeImplicit attitudes are evaluations that occur without conscious awareness towards an attitude object or the self. These evaluations are generally either favorable or unfavorable and come about from various influences in the individual experience. The commonly used definition of implicit attitude within cognitive and social psychology comes from Anthony Greenwald and Mahzarin Banaji's template for definitions of terms related to implicit cognition: "Implicit attitudes are introspectively unidentified (or inaccurately identified) traces of past experience that mediate favorable or unfavorable feeling, thought, or action toward social objects".
BiasBias is a disproportionate weight in favor of or against an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, or a belief. In science and engineering, a bias is a systematic error. Statistical bias results from an unfair sampling of a population, or from an estimation process that does not give accurate results on average. The word appears to derive from Old Provençal into Old French biais, "sideways, askance, against the grain".
Implicit cognitionImplicit cognition refers to cognitive processes that occur outside conscious awareness or conscious control. This includes domains such as learning, perception, or memory which may influence a person's behavior without their conscious awareness of those influences. Implicit cognition is everything one does and learns unconsciously or without any awareness that one is doing it. An example of implicit cognition could be when a person first learns to ride a bike: at first they are aware that they are learning the required skills.
StereotypeIn social psychology, a stereotype is a generalized belief about a particular category of people. It is an expectation that people might have about every person of a particular group. The type of expectation can vary; it can be, for example, an expectation about the group's personality, preferences, appearance or ability. Stereotypes are sometimes overgeneralized, inaccurate, and resistant to new information, but can sometimes be accurate. An explicit stereotype refers to stereotypes that one is aware that one holds, and is aware that one is using to judge people.
EducationEducation is the transmission of knowledge, skills, and character traits. There are many debates about its precise definition, for example, about which aims it tries to achieve. A further issue is whether part of the meaning of education is that the change in the student is an improvement. Some researchers stress the role of critical thinking to distinguish education from indoctrination. These disagreements affect how to identify, measure, and improve forms of education.
Hindsight biasHindsight bias, also known as the knew-it-all-along phenomenon or creeping determinism, is the common tendency for people to perceive past events as having been more predictable than they were. People often believe that after an event has occurred, they would have predicted or perhaps even would have known with a high degree of certainty what the outcome of the event would have been before the event occurred.
Bias–variance tradeoffIn statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set: The bias error is an error from erroneous assumptions in the learning algorithm.
Cognitive biasA cognitive bias is a systematic pattern of deviation from norm or rationality in judgment. Individuals create their own "subjective reality" from their perception of the input. An individual's construction of reality, not the objective input, may dictate their behavior in the world. Thus, cognitive biases may sometimes lead to perceptual distortion, inaccurate judgment, illogical interpretation, and irrationality. While cognitive biases may initially appear to be negative, some are adaptive.
Self-serving biasA self-serving bias is any cognitive or perceptual process that is distorted by the need to maintain and enhance self-esteem, or the tendency to perceive oneself in an overly favorable manner. It is the belief that individuals tend to ascribe success to their own abilities and efforts, but ascribe failure to external factors. When individuals reject the validity of negative feedback, focus on their strengths and achievements but overlook their faults and failures, or take more credit for their group's work than they give to other members, they are protecting their self-esteem from threat and injury.
Bias of an estimatorIn statistics, the bias of an estimator (or bias function) 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. In statistics, "bias" is an property of an estimator. Bias is a distinct concept from consistency: consistent estimators converge in probability to the true value of the parameter, but may be biased or unbiased; see bias versus consistency for more.
