In medical research, observational studies do not allow investigators to have control over treatment assignment.  As a result, covariates (i.e. age, sex) between treatment groups may differ significantly, which causes biased estimates of treatment effects.
For example, when analyzing the differences in outcomes among patients that received a lung transplant in a study versus those that did not, the lung transplant cohort may have been older and had a lower body weight than the cohort that did not receive a transplant.  Since the two cohorts were unbalanced on these two covariates, the estimate for lung transplant will be biased.<unk> s help to eliminate this bias.
Propensity score
Surrogate endpoint
Stratified sampling
Sample size
Block design
Meta-analysis
Agent study
Intention to treat analysis