Credibility Crisis in Science with Thomas Plümper and Eric Neumayer

About this episode

Thomas Plümper and Eric Neumayer discuss the challenges and misconduct in modern science, including fraud detection and the role of AI.

In this episode, Thomas Plümper and Eric Neumayer explore the hidden challenges in modern science, from outright fraud to the subtler practice of “tweaking” data that distorts results. They examine why the self-correcting nature of science often falls short, how incentives and academic pressure drive misconduct, and the double-edged role of AI in both enabling and detecting fraud. The conversation also tackles debates around p-values and statistical reasoning, shares cautionary case studies, and proposes solutions like greater data transparency and stronger verification standards. Chapters 00:00 Introduction to Fraud in Research 06:21 The Nature of Fraud Detection 08:56 Incentives and Motivations for Fraud 10:43 Self-Correction in Science 12:13 Understanding Statistical Significance 13:04 The Role of Replication in Research 14:32 Bayesian vs Frequentist Approaches 23:09 Understanding Bayesian Statistics and Its Implications 26:24 The Humility of Empirical Science 27:16 Concrete Examples of Scientific Fraud 32:52 Proposed Solutions to Scientific Fraud 34:50 The Reality of Scientific Fraud and Human Nature Guest Links You can purchase their book here ( https://amzn.to/3Ole3lY )…

People in this episode

Hosts: Autumn Phaneuf, Noah Giansiracusa

Guests: Thomas Plümper, Eric Neumayer

Topics covered

• scientific fraud
• data transparency
• statistical reasoning
• AI in science
• self-correction in science
• incentives for misconduct

Keywords

• fraud in research
• p-values
• Bayesian statistics
• replication in research
• academic pressure

Mentioned in this episode

Organizations: Breaking Math, LinkedIn, Substack, Twitter, Instagram, Bluesky

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