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Related Experiment Video

Updated: Jul 1, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Predictor combination in binary decision-making situations.

Robert E McGrath¹

¹Fairleigh Dickinson University, Teaneck, NJ 07666, USA. mcgrath@fdu.edu

Psychological Assessment

|September 10, 2008

Summary

Adding more predictors doesn't always improve decision accuracy in psychology. Often, the best single predictor performs better than multiple combined predictors in real-world scenarios.

Area of Science:

Psychology
Decision Science
Behavioral Economics

Background:

Professional psychologists frequently make binary decisions.
Decision accuracy is often assumed to improve with more predictors.

Purpose of the Study:

To evaluate the actual impact of combining predictors on decision accuracy in applied psychological settings.
To compare the efficacy of multiple predictors against single best predictors.

Main Methods:

Analysis of decision-making processes in typical applied settings.
Comparison of outcomes from combined predictors versus single predictors.

Main Results:

Multiple predictors, when combined using common methods, often do not improve accuracy over the best single predictor.

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A Real-world What-Where-When Memory Test

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Last Updated: Jul 1, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Real-world What-Where-When Memory Test

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

The best single predictor can outperform combined multiple predictors in many practical situations.

Conclusions:

Standard statistical methods may overestimate accuracy gains from additional predictors.
Information required to ensure improved fit with multiple predictors is seldom available.
Findings align with the "take the best" heuristic, suggesting simpler models can be superior.