, s/Lectur e 18.pdf, http ://carcin.oxfordjournals.org/content/19/12/2063.full.pdfBias is any systematic error in the determination of the association between the exposure and disease.A statistic is biased if it is calculated in such a way that it 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:Confounding bias: In statistics, a confounding variable (also confounding factor, lurking variable, a confound, or confounder) is an extraneous variable in a statistical model that correlates (positively or negatively) with both the dependent variable and the independent variable. This bias is removed by matching in case control studies.Selection bias: The error introduced when the study population does not represent the target population. Examples of selection bias areSampling bias is systematic error due to a non-random sample of a population. It is mostly classified as a subtype of selection bias, sometimes specifically termed sample selection bias, but some classify it as a separate type of bias.Healthcare access bias: when the patients admitted to an institution do not represent the cases originated in the communityCONFOUNDINGNON CONFOUNDINGCigarette Smoking - Pre Cancerous Lung Cells - Lung CancerBerkesonian bias: first described by Berkson in 1946 for case-control studies. It is produced when the probability of hospitalisation of cases and controls differ.Neyman bias (synonyms: incidence-prevalence bias, selective survival bias): This type of bias can occur in cross-selectional and case-control studies when asymptomatic, mild, clinically resolved, or fatal cases are inadvertently excluded from the case group because the selected cases are examined some time after the disease process has already begun (i.e. looking at prevalent versus incident cases),Healthy worker effect:Volunteer bias is another type of selection bias. It is due to the fact that those individuals who take part in epidemiologic studies tend to be systematically different from those who do not.Loss to follow-up bias is a form of selection bias that may occur when there are significant losses to follow-up in ongoing cohort and experimental studies. If the final sample is systematically different from the original or target population with regard to exposure frequency or outcome susceptibility, then spurious associations can result.III Jnformation/Observation bias/ Measurement Bias: occurs when information is collecteddifferently between two groups, leading to an error in the conclusion of the association. Wheninformation is incorrect, there is mis classification. Examples of Information Bias areMemory/Recall bias: When cases and control are asked questions about their past history, it may be more likely for the cases to recall the existence of certain event or factors than the control who are healthy persons. Recall bias is a type of measurement bias that is very common in case-control studies. It can be reduced using blindingInterviewer bias: Interviewer knows the status of the subjects before the interview process. Interviewer may probe differently about exposures in the past if he or she knows the subjects as cases. Interviewer bias is a type of measurement bias that can arise in all types of epidemiologic studies. It can be reduced using blindingLead time bias is the bias that occurs when two tests for a disease are compared, and one test (the new, experimental one) diagnoses the disease earlier, but there is no effect on the outcome of the disease--it may appear that the test prolonged survival, when in fact it only resulted in earlier diagnosis when compared to traditional methods.Hawthorne effect: described in the 1920s in the Hawthorne plant of the Western Electric Company (Chicago, IL). It is an increase in productivity--or other outcome under study-- in participants who are aware of being observedSurveillance bias/Detection bias: The group with the known exposure or outcome may be followed more closely or longer than the comparison group.Misclassification bias: When subjects are erroneously classified with respect to either exposure or disease status.Differential misclassification occurs when the level of misclassification differs between the two groups,Random or Non-differential misclassification occurs when the level of misclassification does not differ between the two groups.">