What is Randomized Controlled Experiment?

Randomized Controlled Experiment is a mental model used for better thinking and decision-making.

How do you apply Randomized Controlled Experiment?

To apply Randomized Controlled Experiment, identify situations where this framework is relevant, then use it as a lens to evaluate your options and decisions. The model is most useful when combined with other complementary mental models.

What category does Randomized Controlled Experiment fall under?

Randomized Controlled Experiment falls under the General Thinking & Meta-Models category of mental models. Other models in this category can be found on the General Thinking & Meta-Models hub page.

Why is Randomized Controlled Experiment important?

Randomized Controlled Experiment is important because it provides a structured way to think about problems that would otherwise be approached with intuition alone. Understanding this model helps you avoid common reasoning errors and make better decisions.

General Thinking & Meta-Models

Randomized Controlled Experiment

Model #0364Category: General Thinking & Meta-ModelsDepth to apply:

15 min read

Contents

1. The Core Idea
2. How to See It
3. How to Use It
4. The Mechanism
5. Founders & Leaders in Action
6. Visual Explanation
7. Connected Models
8. One Key Quote
9. Analyst's Take
10. Test Yourself
11. Summary & Further Reading

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Section 1

The Core Idea

A randomized controlled experiment (RCT) is the gold standard for learning whether something causes an outcome. You take a population, randomly assign units to treatment (get the intervention) or control (don't), then compare outcomes. Random assignment balances the groups on average — known and unknown factors — so any difference in outcomes can be attributed to the treatment. Observational data can show correlation; RCTs can support causation. The method comes from medicine and agriculture; it is now central in tech (A/B tests), policy (evidence-based programmes), and product development.

RCTs have limits: they're not always feasible (you can't randomise some interventions), they measure average effects (heterogeneity may matter), and they can be expensive or slow. When feasible, they're the best way to answer "did X cause Y?" The strategic discipline is to use RCTs when the question is causal and the intervention can be randomised, and to use other methods when it can't. Don't confuse correlation with causation; when the stakes are high, run an experiment.