Lecture Notes on Scientific Fraud by Dr. Suzanne Shale

Introduction

• Dr. Suzanne Shale, ethics advisor to the English NHS, discussed the issue of scientific fraud.
• Focus on healthcare ethics, whistleblowing in healthcare, and her work with NHS Scotland.

Key Themes

• Importance of creativity and rigor in science.
• The historical and ongoing issue of scientific fraud.
• Three case studies across different disciplines.

Science and Fraud

• Science requires a balance of creativity and rigor.
• Problems arise when creativity surpasses rigor.
• Issue of scientists creating data to support their hypotheses, rather than seeking falsifiable data.

Historical Context

• Charles Babbage in 1830 highlighted issues of scientific fraud.
• Terms "cooking" and "trimming" to describe data manipulation.

Case Studies

1. Emil Rupp (Physics)

• Worked in Germany during the 1920s and 30s.
• Falsified data for 10 years, misleading scientists including Einstein.
• Dismissed after reproducibility checks and whistleblowing.

Lessons from Rupp's Case

• Reproducibility acts as a safeguard.
• Importance of prompt employer response to allegations.

2. Eric Poilman (Medical Research)

• First US scientist imprisoned for scientific fraud.
• Falsified a longitudinal study on menopause, LDL/HDL data.
• Exposed by lab technician Walter D'Ono after colleagues and professors failed to act.

Lessons from Poilman's Case

• Effective whistleblowing can lead to uncovering fraud.
• Importance of proper advice to potential whistleblowers.
• Misleading data can suppress valuable true findings.

3. Bengu Sezen (Chemistry)

• Conducted fraud at Columbia University.
• Falsified NMR data, used fictitious entities for validation.
• Uncovered by a junior researcher through a sting operation.

Lessons from Sezen's Case

• Reproducibility checks can help uncover fraud.
• Fraud has devastating impacts on peers and lab dynamics.

Understanding Scientific Fraud

• Various theories:
  1. Dishonesty and sociopathic behavior.
  2. Research environment pressures.
  3. Cognitive bias and confirmation bias.
  4. Small steps leading to larger frauds.

Ethics and Social Structures

• Modern science pressures lead to data manipulation.
• Historical cases like Babbage indicate long-standing issues.

Cognitive Bias in Science

• Example: NECA cube and how perception can be manipulated.
• Temptation to see data that supports preferred hypotheses.

Pyramid of Choice

• Small ethical compromises can escalate into major fraud.
• Explanation using cognitive dissonance.

Conclusion

• Fraud evolves from self-delusion to fraud through imperceptible steps.
• The need for awareness and vigilance in scientific ethics.

Q&A Highlights

• Discussion on distinguishing poor science from fraud.
• Importance of publication bias and integrity in scientific publication.
• Ethical implications of using social media to expose fraud.

Final Thoughts

• Fraud in science poses a significant threat to scientific integrity and public trust.
• Efforts needed to balance creativity and rigor while maintaining ethical standards.