Enhancing Scientific Self-Correction

📋 Course Outline

1. Myth of self-correcting science
2. Retractions and honest errors
3. Types of honest errors
4. Error detection techniques
5. Statcheck and reporting inconsistencies
6. GRIM, randomization and blinding checks
7. Measurement practices and statistical power
8. Data anomalies and self-correction
9. Global solutions and good practices

📖 1. Myth of self-correcting science

🔑 Key Concepts & Definitions

• Error-prone humans: A description of how people inevitably make mistakes in thinking, measurement, and data handling.
• Self-correction myth: The belief that scientific progress automatically corrects false or flawed findings without deliberate action.
• Loss-of-Confidence survey: A survey of psychology researchers about barriers that prevent collective correction after loss of confidence in findings.

📝 Essential Points

• Science does not self-correct by default, so researchers must actively challenge and repair the record.
• A survey reported 44% of respondents lost confidence in at least one finding.
• Among those, 56% attributed the loss to researchers’ mistake or judgment shortcomings, and 28% took primary responsibility for the error.

💡 Memory Hook

Self-correction is not automatic: confidence loss must trigger action.

📖 2. Retractions and honest errors

🔑 Key Concepts & Definitions