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Strong theory testing using the prior predictive and the data prior.

Wolf Vanpaemel¹

¹University of Leuven.

Psychological Review

|December 31, 2019

Summary

This summary is machine-generated.

Theory testing requires more than just a good fit to data. A strong theory test, which supports a theory, must demonstrate that the theory could have plausibly failed the test. Complexity alone is insufficient for this assessment.

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Area of Science:

Cognitive Psychology
Philosophy of Science

Background:

Roberts and Pashler (2000) established that a good fit to empirical data does not automatically validate a theory.
The persuasiveness of a theory's fit depends on surviving a strong test, where failure was a plausible outcome.

Purpose of the Study:

To argue against using complexity as a sole measure for the severity of a theory test.
To propose that demonstrating the plausibility of a bad fit is crucial for a strong test.

Main Methods:

Critique of the use of complexity in evaluating theory testing.
Introduction of the concept of a 'data prior' to quantify plausible outcomes before data analysis.

Main Results:

Complexity measures inform about the possibility of a bad fit but do not guarantee a strong test.
A strong test requires demonstrating that a bad fit was plausible.

Conclusions:

While complexity is useful in theory testing, it is misguided to use it to gauge test severity or fit persuasiveness.
A 'data prior' is essential for a complete assessment of whether a good fit is persuasive, ensuring strong theory testing.