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Present bias is the tendency to settle for a smaller present reward rather than wait for a larger future reward, in a trade-off situation. [ 1 ] [ 2 ] It describes the trend of overvaluing immediate rewards, while putting less worth in long-term consequences. [ 3 ]
A different form of dynamic inconsistency arises as a consequence of "projection bias" (not to be confused with a defense mechanism of the same name). Humans have a tendency to mispredict their future marginal utilities by assuming that they will remain at present levels. This leads to inconsistency as marginal utilities (for example, tastes ...
In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding). [8] They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as:
In Canadian law, a reasonable apprehension of bias is a legal standard for disqualifying judges and administrative decision-makers for bias. Bias of the decision-maker can be real or merely perceived. The test was first stated in Committee for Justice and Liberty v. Canada (National Energy Board), [1978] 1 S.C.R. 369:
Law of Indonesia is based on a civil law system, intermixed with local customary law and Dutch law.Before European presence and colonization began in the sixteenth century, indigenous kingdoms ruled the archipelago independently with their own custom laws, known as adat (unwritten, traditional rules still observed in the Indonesian society). [1]
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.
A typical measure of bias of forecasting procedure is the arithmetic mean or expected value of the forecast errors, but other measures of bias are possible. For example, a median-unbiased forecast would be one where half of the forecasts are too low and half too high: see Bias of an estimator .
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).