urvalsbias

urvalsbias, Swedish for selection bias, refers to systematic errors that occur when the participants or items chosen for a study are not representative of the target population. This bias can arise from various sources such as non‑random sampling, voluntary response, attrition, or researcher expectations influencing inclusion criteria. The result is that findings may systematically overestimate or underestimate true relationships, limiting the external validity of conclusions.

In epidemiological research, selection bias frequently manifests when exposure and outcome data are collected in a way that preferentially includes certain subgroups. For example, a survey about diet that only recruits participants from health‑food stores may overrepresent healthier individuals. In clinical trials, loss to follow‑up can create differential drop‑out rates that distort treatment effect estimates. Social science studies often face similar problems when respondents self‑select into online panels, producing skewed demographic profiles.

Statistical techniques can mitigate urvalsbias. Randomized designs, probability sampling, weighting adjustments, and sensitivity analyses for missing data can improve representativeness. Researchers may also conduct external validation by comparing study samples to population benchmarks. Documenting inclusion and exclusion criteria, recruitment channels, and response rates helps readers assess the potential magnitude of bias.

Recognizing and addressing urvalsbias is essential to preserve the integrity of scientific research. By systematically evaluating how samples are assembled and applying corrective methods, scholars can reduce bias and enhance the reliability of their findings.