A statistic is biased if it is calculated in such a way that is systematically different from the population parameter of interest. The following lists some types of, or aspects of, bias which should not be considered mutually exclusive:
The bias of an estimator is the difference between an estimator's expectations 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 is less than or equal to the significance level when the null hypothesis is true, and the probability of rejecting the null hypothesis is greater than or equal to the significance level when the alternative hypothesis is true,
Detection bias is where a phenomenon is more likely to be observed and/or reported 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 less overweight patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
Funding bias may lead to selection of outcomes, test samples, or test procedures that favor a study's financial sponsor.
Reporting bias involves a skew in the availability of data, such that observations of a certain kind may be more likely to be reported and consequently used in research.