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Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Related Experiment Video

Updated: Jun 3, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Functional adaptive sequential testing.

Edward Vul¹, Jacob Bergsma, Donald I A MacLeod

¹Department of Psychology, University of California, San Diego, USA. evul@ucsd.edu

Seeing and Perceiving

|April 7, 2011

Summary

This summary is machine-generated.

This study introduces Functional Adaptive Sequential Testing (FAST), a new method for efficiently estimating thresholds across various conditions. FAST improves accuracy by considering how thresholds relate to independent variables, optimizing cognitive and behavioral research.

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Last Updated: Jun 3, 2026

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Area of Science:

Cognitive science
Psychophysics
Behavioral neuroscience

Background:

Estimating thresholds is crucial for understanding cognition, perception, and behavior.
Traditional unidimensional adaptive testing is efficient for single thresholds but can be improved.

Purpose of the Study:

To present a generic method, Functional Adaptive Sequential Testing (FAST), for efficiently estimating thresholds as a function of an independent variable.
To enhance the efficiency of threshold estimation by leveraging the relationship between thresholds at different independent variable values.

Main Methods:

Formal description of the FAST algorithm.
Introduction of a Matlab toolbox implementation for FAST.
Evaluation of various sampling and estimation algorithms for two-dimensional threshold functions.

Main Results:

Demonstrated substantial efficiency gains in threshold estimation.
Showcased the benefit of considering functional relationships between thresholds.
Validated the FAST method for estimating parameters relating independent variables to thresholds.

Conclusions:

FAST offers a more efficient approach to threshold estimation in psychophysics and cognitive science.
The method is valuable for research involving continuous independent variables and their effect on thresholds.
The developed toolbox facilitates the application of FAST in empirical research.