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In economics, money illusion, or price illusion, is a cognitive bias where money is thought of in nominal, rather than real terms. In other words, the face value (nominal value) of money is mistaken for its purchasing power (real value) at a previous point in time.
Economic variables are not readily isolated for experimental testing, but Edward Leamer argues that there is no essential difference between econometric analysis and randomized trials or controlled trials provided the use of statistical techniques reduces the specification bias, the effects of collinearity between the variables, to the same ...
Media bias is the bias or perceived bias of journalists and news producers within the mass media in the selection of events, the stories that are reported, and how they are covered. The term generally implies a pervasive or widespread bias violating the standards of journalism , rather than the perspective of an individual journalist or article ...
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Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
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). All else being equal, an unbiased estimator is preferable to a biased estimator, although in practice, biased estimators (with generally small bias ...
Inflationary bias is the outcome of discretionary monetary policy that leads to a higher than optimal level of inflation. Depending on the way expectations are formed in the private sector of the economy, there may or may not be a transitory income increase.
A variable omitted from the model may have a relationship with both the dependent variable and one or more of the independent variables (causing omitted-variable bias). [3] An irrelevant variable may be included in the model (although this does not create bias, it involves overfitting and so can lead to poor predictive performance).