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Field experiments are experiments carried out outside of laboratory settings.
They randomly assign subjects (or other sampling units) to either treatment or control groups in order to test claims of causal relationships. Random assignment helps establish the comparability of the treatment and control group, so that any differences between them that emerge after the treatment has been administered plausibly reflect the influence of the treatment rather than pre-existing differences between the groups. The distinguishing characteristics of field experiments are that they are conducted real-world settings and often unobtrusively and control not only subject pool but selection and overtness, as defined by leaders such as John A. List. This is in contrast to laboratory experiments, which enforce scientific control by testing a hypothesis in the artificial and highly controlled setting of a laboratory. Field experiments have some contextual differences as well from naturally-occurring experiments and quasi-experiments. While naturally-occurring experiments rely on an external force (e.g. a government, nonprofit, etc.) controlling the randomization treatment assignment and implementation, field experiments require researchers to retain control over randomization and implementation. Quasi-experiments occur when treatments are administered as-if randomly (e.g. U.S. Congressional districts where candidates win with slim-margins, weather patterns, natural disasters, etc.).
Field experiments encompass a broad array of experimental designs, each with varying degrees of generality. Some criteria of generality (e.g. authenticity of treatments, participants, contexts, and outcome measures) refer to the contextual similarities between the subjects in the experimental sample and the rest of the population. They are increasingly used in the social sciences to study the effects of policy-related interventions in domains such as health, education, crime, social welfare, and politics.