Epidemiological research is inherently subject to various sources of error that can distort the estimation of associations between exposures and outcomes. This chapter distinguishes between random error—the result of chance variation—and systematic error (bias), which consistently skews results away from the truth. Key types of bias are examined, including selection bias, arising from non-representative participant inclusion, and information bias, resulting from inaccurate measurement or classification. The chapter also explores confounding, where the observed association is distorted by the influence of extraneous variables, and interaction, in which the effect of one factor depends on the presence of another. Understanding these concepts is essential for critically appraising epidemiological evidence and designing studies that yield valid and reliable findings.