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Bias is a term used to describe a tendency or preference towards a particular perspective, ideology, or result, when the tendency interferes with the ability to be impartial, unprejudiced, or objective.[1]. In other words, bias is generally seen as a 'one-sided' perspective. The term biased refers to a person or group who is judged to exhibit bias. It is used to describe an attitude, judgment, or behavior that is influenced by a prejudice. Bias can be unconscious or conscious in awareness. Labeling someone as biased in some regard implies that they need a greater or more flexible perspective in that area, or that they need to consider the context more deeply.

In psychology

In psychology, cognitive bias is bias based on factors related to the brain as an information processor. One type of cognitive bias is confirmation bias, the tendency to interpret new information in such a way that confirms one's prior beliefs, even to the extreme of denial, ignoring information that conflicts with one's prior beliefs. The fundamental attribution error, also known as "correspondence bias", is one example of such bias, in which people tend to explain others' behavior in terms of personality, whereas they tend to explain their own behavior in terms of the situation.[2][3]

If something is 'biased' or an opinion is 'biased', then that just means that the information is of one side. It does not mean that the information is not accurate at all.

In statistics

Main article: Bias (statistics)

In statistics, there are several types of bias:

  • Selection bias, where there is an error in choosing the individuals or groups to take part in a scientific study. It includes sampling bias, in which some members of the population are more likely to be included than others. Spectrum bias consists of evaluating the ability of a diagnostic test in a biased group of patients, which leads to an overestimate of the sensitivity and specificity of the test.
  • The bias of an estimator is the difference between an estimator's expectation and the true value of the parameter being estimated. Omitted-variable bias is the bias that appears in estimates of parameters in a regression analysis when the assumed specification is incorrect, in that it omits an independent variable that should be in the model.
  • In statistical hypothesis testing, a test is said to be unbiased when the probability of rejecting the null hypothesis exceeds the significance level when the alternative is true and is less than or equal to the significance level when the null hypothesis is true.
  • Systematic bias or systemic bias are external influences that may affect the accuracy of statistical measurements.
  • Data-snooping bias comes from the misuse of data mining techniques.

Other aspects

  • Cultural: interpreting and judging phenomena in terms particular to one's own culture.
  • Ethnic or racial: racism, regionalism and tribalism.
  • Geographical: describing a dispute as it is conducted in one country, when the dispute is framed differently elsewhere.
  • Inductive bias in machine learning
  • Media: real or perceived bias of journalists and news producers within the mass media, in the selection of which events will be reported and how they are covered
  • Gender: including sexism and heteronormativity.
  • Linguistic: bias, favoring certain languages
  • Political: bias in favor of or against a particular political party, philosophy, policy or candidate.
  • Corporate: bias in favor of a business.
  • Advertising: bias with observations motivated for selling an opinion rather than using objectivity.
  • Sociological: bias in favor of a society's ideals. bias for groups needs/wants.
  • Personal: bias for personal gain.
  • Religious: bias for or against religion, faith or beliefs;
  • Sensationalist: favoring the exceptional over the ordinary. This includes emphasizing, distorting, or fabricating exceptional news to boost commercial ratings.
  • Scientific (including anti-scientific and scientific skepticism): favoring (or disfavoring) a scientist, inventor, or theory for non-scientific reasons. This can also include excessive favoring (or disfavoring) prevalent scientific opinion, if in doing so, other viewpoints are no longer being treated neutrally

See also


External links

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