Observational data are often used for research in critical care. Unlike randomized controlled trials, where randomization theoretically balances confounding factors, studies involving observational data pose the challenge of how to adjust appropriately for the bias and confounding that are inherent when comparing two or more groups of patients. This paper first highlights the potential sources of bias and confounding in critical care research and then reviews the statistical techniques available (matching, stratification, multivariable adjustment, propensity scores, and instrumental variables) to adjust for confounders. Finally, issues that need to be addressed when interpreting the results of observational studies, such as residual confounding, causality, and missing data, are discussed.