Index of biases


 * Admission rate bias Arises when the variables under study are affected by the selection of hospitalized subjects leading to a bias between the exposure and the disease under study
 * All’s well literature bias Occurs when publications omit or play down controversies or disparate results
 * Allocation bias Systematic difference in how participants are assigned to treatment and comparison groups in a clinical trial
 * Apprehension bias Certain measures (pulse. blood pressure) may alter systematically from their usual levels when the subject is apprehensive
 * Ascertainment bias Systematic differences in the identification of individuals included in a study or distortion in the collection of data in a study
 * Attrition bias Unequal loss of participants from study groups in a trial
 * Biases of rhetoric An argument used to persuade the reader without appealing to reason or evidence
 * Bogus control bias When patients who are allocated to an experimental manoeuvre die or sicken before or during its administration and are omitted or re-allocated to the control group. the experimental manoeuvre will appear spuriously superior
 * Centripetal bias The reputations of certain clinicians and institutions cause individuals with specific disorders or exposures to gravitate toward them
 * Chronological bias When study participants allocated earlier to an intervention or a group are subject to different exposures or are at a different risk from participants who are recruited later
 * Compliance bias  Participants compliant with an intervention differ in some way from those not compliant which can systematically affect the outcome of interest
 * Confirmation bias The search for and use of information to support an individual’s ideas, beliefs or hypotheses
 * Confounding    A distortion that modifies an association between an exposure and an outcome because a factor is independently associated with the exposure and the outcome
 * Confounding by indication A distortion that modifies an association between an exposure and an outcome, caused by the presence of an indication for the exposure that is the true cause of the outcome
 * Contamination bias In an experiment when members of the control group inadvertently receive the experimental manoeuvre. the difference in outcomes between experimental and control patients may be systematically reduced
 * Correlation bias Equating correlation with causation leads to errors of both kinds
 * Data dredging bias When data are reviewed for all possible associations without prior hypothesis, the results are suitable for hypothesis-forming activities only
 * Detection bias Systematic differences between groups in how outcomes are determined
 * Diagnostic access bias Individuals differ in their geographic, temporal and economic access to diagnostic procedures which label them as having a given disease
 * Diagnostic purity bias When ‘pure’ diagnostic groups exclude co-morbidity they may become non-representative
 * Diagnostic suspicion bias Knowledge of a subject’s prior exposures or personal biases may influence both the process and the outcome of diagnostic tests
 * Diagnostic vogue bias The same illness may receive different diagnostic labels at different points in space or time
 * End-digit preference bias In converting analog to digital data observers may record some terminal digits with an unusual frequency
 * Expectation bias Observers may systematically err in measuring and recording observations so that they concur with prior expectations
 * Exposure suspicion bias A knowledge of the subject’s disease status may influence both the intensity and outcome of a search for exposure to the putative cause
 * Family information bias The flow of family information about exposure and illness is stimulated by and directed to, a new case in its midst
 * Hawthorne effect When individuals modify an aspect of their behaviour in response to their awareness of being observed
 * Hot stuff bias When a topic is fashionable (‘hot’)  investigators may be less critical in their approach to their research, and investigators and editors may not be able to resist the temptation to publish the results
 * Informed presence bias The presence of a person’s information in an electronic health record is affected by the person’s health status
 * Insensitive measure bias When outcome measures are incapable of detecting clinically significant changes or differences, Type II errors occur
 * Instrument bias Defects in the calibration or maintenance of measurement instruments may lead to systematic deviations from true values
 * Language bias Publication of research findings in a particular language
 * Magnitude bias In interpreting a finding the selection of a scale of measurement may markedly affect the interpretation
 * Migrant bias Migrants may differ systematically from those who stay home
 * Membership bias Membership in a group (the employed joggers, etc.) may imply a degree of health which differs systematically from that of the general population
 * Mimicry bias An innocent exposure may become suspicious if, rather than causing disease, it causes a benign disorder which resembles the disease
 * Misclassification bias When a study participant is categorised into an incorrect category altering the observed association or research outcome of interest
 * Missing clinical data bias Missing clinical data may be missing because they are normal, negative, never measured. or measured but never recorded
 * Mistaken identity bias In compliance trials, strategies directed toward improving the patient’s compliance may. instead or in addition, cause the treating clinician to prescribe more vigorously: the effect upon achievement of the treatment goal may be misinterpreted
 * Non-contemporaneous control bias Differences in the timing of selection of case and controls within in a study influence exposures and outcomes resulting in biased estimates
 * Non-respondent bias Non-respondents (or ‘late comers’) from a specified sample may exhibit exposures or outcomes which differ from those of respondents (or ‘early comers’)
 * Novelty bias The tendency for an intervention to appear better when it is new
 * Obsequiousness bias Subjects may systematically alter questionnaire responses in the direction they perceive desired by the investigator
 * Observer bias The process of observing and recording information which includes systematic discrepancies from the truth
 * One-sided reference bias When authors restrict their references to only those works that support their position
 * Outcome reporting bias The selective reporting of pre-specified outcomes in published clinical trials
 * Perception bias The tendency to be subjective about people and events, causing biased information to be collected in a study or biased interpretation of a study’s results
 * Popularity bias Differences in the uptake of healthcare as a result of a public interest in a disease or condition and its possible causes results in a biased study sample
 * Positive results bias The tendency to submit, accept and publish positive results rather than non-significant or negative results
 * Post hoc significance bias When decision levels or ‘tails’ for z and /3 are selected offer the data have been examined, conclusions may be biased
 * Prevalence-incidence (Neyman) bias Exclusion of individuals with severe or mild disease resulting in a systematic error in the estimated association or effect of an exposure on an outcome
 * Previous opinion bias The results of a previous assessment, test result or diagnosis, if known, may affect the results of subsequent processes on the same patient
 * Procedure selection bias Certain clinical procedures may be preferentially offered to those who are poor risks
 * Publication bias The selective publication of positive findings from randomized controlled trials
 * Recall bias Systematic error due to differences in accuracy or completeness of recall to memory of past events or experiences
 * Referral filter bias As a group of ill are referred from primary to secondary to tertiary care, the concentration of rare causes. multiple diagnoses and ‘hopeless cases’ may increase
 * Repeated peeks bias Repeated peeks at accumulating data in a randomized trial are not dependent. and may lead to inappropriate termination
 * Scale degradation bias The degradation and collapsing of measurement scales tends to obscure differences between groups under comparison
 * Selection bias Occurs when individuals or groups in a study differ systematically from the population of interest leading to a systematic error in an association or outcome
 * Significance bias The confusion of statistical significance, on the one hand, with biologic or clinical or health care significance, on the other hand, can lead to fruitless studies and useless conclusions
 * Starting time bias The failure to identify a common starting time for exposure or illness may lead to systematic misclassification
 * Substitution game The substitution of a risk factor which has not been established as causal for its associated outcome
 * Therapeutic personality bias When treatment is not ‘blind’, the therapist’s convictions about efficacy may systematically influence both outcomes (positive personality) and their measurement (desire for positive results)


 * Tidying up bias The exclusion of outliers or other untidy results cannot be justified on statistical grounds and may lead to bias
 * Unacceptability bias Measurements which hurt, embarrass or invade privacy may be systematically refused or evaded
 * Unacceptable disease bias Lower rates of reporting of certain “unacceptable” diseases compared with other health conditions
 * Under-exhaustion bias The failure to exhaust the hypothesis space may lead to authoritarian rather than authoritative interpretation
 * Underlying cause (rumination) bias Cases may ruminate about possible causes for their illnesses and thus exhibit different recall or prior exposures than controls
 * Unmasking (detection signal) bias An innocent exposure may become suspect if, rather than causing a disease, it causes a sign or symptom which precipitates a search for the disease
 * 'Verification bias' In a diagnostic accuracy study, confirmation of a diagnosis is not consistent between groups of people: whilst some participants receive confirmation of the diagnosis by one reference standard, some have no confirmation, or have confirmation by a different reference standard
 * Volunteer bias Participants volunteering to take part in a study intrinsically have different characteristics from the general population of interest
 * Withdrawal bias Patients who are withdrawn from an experiment may differ systematically from those who remain
 * Wrong sample size bias When the wrong sample size is used in a study: small sample sizes often lead to chance findings, while large sample sizes are often statistically significant but not clinically relevant