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Practical Methodology of Cognitive Tasks Within a Navigational Assessment

Practical Methodology of Cognitive Tasks Within a Navigational Assessment

Published on: June 1, 2015

Invited Commentary: The Need for Cognitive Science in Methodology.

Sander Greenland¹

¹Department of Epidemiology, School of Public Health, University of California, Los Angeles, CA.

American Journal of Epidemiology

|September 24, 2017

Summary

Statistical training must address cognitive biases like dichotomania and nullism, which distort research findings. Addressing these psychosocial factors is crucial for accurate interpretation of study results beyond mechanistic biases.

Keywords:

behavioral economics bias analysis cognitive bias motivated reasoning nullism overconfidence sensitivity analysis significance testing

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Area of Science:

Statistics
Research Methodology
Cognitive Psychology

Background:

Statistical methods are prone to misuse, leading to distorted interpretations.
Cognitive biases and psychosocial factors significantly impact statistical inferences.
Existing solutions like interval estimation and null-hypothesis significance testing have limitations.

Purpose of the Study:

To discuss common cognitive distortions in statistical analysis: dichotomania, nullism, and statistical reification.
To explain how these distortions, particularly when combined with null-hypothesis significance testing, lead to flawed study reporting.
To propose improvements in methodological training and statistical practices.

Main Methods:

Conceptual analysis of cognitive biases affecting statistical interpretation.
Critique of common statistical practices including null-hypothesis significance testing and interval estimation.
Discussion of sensitivity and bias analyses and their limitations.

Main Results:

Dichotomania, nullism, and statistical reification are prevalent cognitive distortions.
Null-hypothesis significance testing exacerbates these distortions, leading to unreliable results.
Interval estimation and current bias analyses can also introduce new distortions.

Conclusions:

Methodological training must extend beyond mechanistic biases to include cognitive and psychosocial factors.
Reframing the use of P values and employing sensitivity analyses can mitigate some distortions.
Developing statistical methods and training that account for cognitive biases is essential for improving research integrity.