They randomly assign subjects (or other sampling units) to either treatment or control groups to test claims of causal relationships.
The distinguishing characteristics of field experiments are that they are conducted in real-world settings and often unobtrusively and control not only the subject pool but selection and overtness, as defined by leaders such as John A.
Quasi-experiments occur when treatments are administered as-if randomly (e.g. U.S. Congressional districts where candidates win with slim margins,[2] weather patterns, natural disasters, etc.).
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.
After designing the field experiment and gathering the data, researchers can use statistical inference tests to determine the size and strength of the intervention's effect on the subjects.
For example, a researcher could design an experiment that uses pre- and post-trial information in an appropriate statistical inference method to see if an intervention has an effect on subject-level changes in outcomes.
Field experiments offer researchers a way to test theories and answer questions with higher external validity because they simulate real-world occurrences.
Given that field experiments necessarily take place in a specific geographic and political setting, there is a concern about extrapolating outcomes to formulate a general theory regarding the population of interest.
These problems can lead to imprecise data analysis; however, researchers who use field experiments can use statistical methods in calculating useful information even when these difficulties occur.
As well, field experiments can adopt a "stepped-wedge" design that will eventually give the entire sample access to the intervention on different timing schedules.