Confounding

In Epidemiology a confounder is:

not part of the real association between exposure and disease
predicts disease
unequally distributed between exposure groups

A researcher can only control a study or analysis for confounders that are:

known
measurable

Example: Grey hair predicts heart disease if it is put into a multiple regression model because it is unequally distributed between people who do have heart disease (the elderly) and those who don't (the young). Grey hair confounds thinking about heart disease because it is not a cause of heart disease.

Strategies to reduce confounding are:

randomization (aim is random distribution of confounders between study groups)
restriction (restrict entry to study of individuals with confounding factors - risks bias in itself)
matching (of individuals or groups, aim for equal distribution of confounders)
stratification (confounders are distributed evenly within each stratum)
adjustment (usually distorted by choice of standard)
multivariate analysis (only works if you can identify and measure the confounders)