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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

A Bayesian perspective on severity: risky predictions and specific hypotheses.

Noah van Dongen¹, Jan Sprenger², Eric-Jan Wagenmakers³

¹University of Amsterdam, Amsterdam, Netherlands. nnnvandongen@gmail.com.

Psychonomic Bulletin & Review

|August 15, 2022

Summary

This summary is machine-generated.

This study re-evaluates statistical hypothesis testing, arguing that severity, or the stringent testing of hypotheses, is crucial for both error-statistical and Bayesian inference. It highlights the importance of research context and specific predictions for robust statistical analysis.

Keywords:

Bayes factors Deborah Mayo Error statistics Karl Popper Null hypothesis significance testing Severity Statistical test

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Area of Science:

Statistics
Philosophy of Science

Background:

Hypothesis testing traditionally values severity, assessing the stringency of attempts to falsify a hypothesis.
Error statisticians emphasize severity, while Bayesian inference is criticized for allegedly neglecting it.

Purpose of the Study:

To critique the error-statistical account of severity for its oversight of research context and prediction specificity.
To demonstrate the relevance and benefits of severity within Bayesian statistical inference.

Main Methods:

Conceptual analysis of statistical inference methodologies.
Illustrative application of severity-based reasoning in a Bayesian hypothesis testing example.

Main Results:

The error-statistical approach to severity has limitations.
Severity enhances the evidential value of Bayesian hypothesis tests through specific, risky predictions.

Conclusions:

Severity is a vital component for both error-statistical and Bayesian hypothesis testing.
Incorporating severity enriches Bayesian inference, offering a more robust approach to statistical evidence.