Bias

Bias is a systematic error that leads to an incorrect estimate of effect or association. Many factors can bias the results of a study such that they cancel out, reduce or amplify a real effect you are trying to describe.

Epidemiology categorises types of bias, examples are:

Selection bias - e.g. study of car ownership in central London is not representative of the UK.
Observation bias (recall and information) - e.g. on questioning, healthy people are more likely to under report their alcohol intake than people with a disease.
Observation bias (interviewer) - e.g. different interviewer styles might provoke different responses to the same question.
Observation bias (misclassification) - tends to dilute an effect
Losses to follow up - e.g. ill people may not feel able to continue with a study whereas health people tend to complete it.

Some strategies to combat bias:

multiple control groups
standardised observations (e.g. blinding (don't know if placebo or active intervention) of subject, observer, both subject and observer (double blind) or subject, observer and analyst (triple blind))
corroboration of multiple information sources
use of dummy variables with known associations