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Conceptualizing Experimental Controls Using the Potential Outcomes Framework.

Kristen B Hunter¹, Kristen Koenig², Marie-Abèle Bind^3,4

¹School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia.

The American Statistician

|November 14, 2025

Summary

This summary is machine-generated.

This study introduces precise definitions for experimental controls, like treatment, outcome, and contrast controls. These definitions aid researchers in designing robust experiments and ensuring reproducible scientific research.

Keywords:

Causal inference Design of experiments Experimental design Negative control Positive control

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Area of Science:

Experimental Design
Statistical Methodology
Scientific Research

Background:

Controlled experiments aim to isolate causal effects by minimizing unwanted variation.
Experimental controls (e.g., negative and positive controls) are crucial for detecting systematic errors.
Existing definitions of controls can lack mathematical precision, hindering pedagogical use and practitioner communication.

Purpose of the Study:

To introduce a taxonomy of mathematically precise definitions for experimental controls.
To provide clear, pedagogical definitions using the potential outcomes framework.
To enhance the understanding and application of experimental controls in scientific research.

Main Methods:

Utilized the potential outcomes framework for defining experimental controls.
Developed a taxonomy categorizing controls into treatment, outcome, and contrast types.
Provided illustrative examples for each defined control type.

Main Results:

Defined three distinct types of experimental controls: treatment, outcome, and contrast controls.
Established mathematically precise definitions for these control types.
Demonstrated the utility of these controls in identifying unwanted variation and potential design flaws.

Conclusions:

Experimental controls are powerful, underutilized tools for improving research rigor.
Precise definitions facilitate better experimental design and detection of flaws.
These definitions support reproducible, replicable, and transparent scientific research.