The Gold Standard for Understanding Cause and Effect in the Real World
Field experiments take the power of a scientific lab into the messy, unpredictable real world. By randomly assigning people to 'treatment' and 'control' groups, we can find out what truly works—from shaping policy to building better products.
Not all experiments are created equal. They exist on a spectrum, trading off the pristine control of a lab for the authentic realism of the field. This choice fundamentally shapes what we can learn.
The central challenge in research is balancing **Internal Validity** (confidence that our intervention caused the outcome) with **External Validity** (confidence that our findings apply to the real world).
Even within "field experiments," there's a range. The Harrison-List typology shows how realism increases as we move from lab-like settings to the truly natural world, which has major implications for ethics and participant awareness.
A lab experiment with real people (not just students). It tests if behavior changes with a relevant subject pool.
Adds real-world context to the tasks and stakes. Participants know they're in a study, but the situation feels more authentic.
The gold standard for realism. Participants are in their everyday environment and completely unaware they're in an experiment.
Running a field experiment is a marathon, not a sprint. It's a rigorous process that combines scientific design with practical project management.
Define a clear, testable question.
Choose who to study and how to randomize.
Secure partners and get IRB approval.
Launch the intervention in the field.
Analyze the data to find the causal effect.
Field experiments have produced groundbreaking insights that changed policy, business, and our understanding of society.
A famous 2004 study sent identical resumes to employers, randomly assigning either a White-sounding or Black-sounding name.
Source: Bertrand & Mullainathan (2004). This Natural Field Experiment provided undeniable causal evidence of discrimination.
A 2000 experiment tested different get-out-the-vote tactics, revolutionizing how political campaigns operate.
Source: Gerber & Green (2000). High-quality personal contact is far more effective than impersonal methods.
Participants dropping out can bias results if rates differ between groups.
The treatment "spills over" and affects the control group, contaminating the comparison.
Without a large enough sample size, a study might fail to detect a real effect.
Using Machine Learning to ask "What works for whom?" and tailor interventions to individuals.
Designing experiments to predict if a successful pilot program will still work when rolled out to millions.