What is sample bias and how does it affect research results?

Sample bias occurs when the research sample is not representative of the target population intended for analysis. This discrepancy distorts research findings and undermines the validity of conclusions drawn from the sample to the population. A sample suffers from bias if systematic selection errors cause segments of the population to be over- or underrepresented. Common types include selection bias, where the sampling method excludes certain groups; self-selection bias, where only individuals motivated to participate enroll; and survival bias, where only entities existing throughout the study period are analyzed. This bias fundamentally affects generalizability and compromises both internal and external validity. Specifically, it can skew statistical estimates, inflate or obscure effect sizes, mask true relationships, and significantly increase the likelihood that research results cannot be replicated. To mitigate sample bias, researchers prioritize methods ensuring representativeness, such as probability sampling using random selection and adequate coverage. Practical limitations often require careful study design to minimize distortion, including recognizing inherent constraints. Applying these principles is crucial across disciplines to yield reliable inferences about the population and avoid erroneous interpretations, enhancing the credibility and utility of research outcomes.