# Correlation Is Not Causation: Learn How to Avoid the 5 Traps That Even Pros Fall Into
## Introduction
- Why correlation doesn’t imply causation
- Common misconception
- Importance of understanding the difference
- The goal of the book
- Avoiding common traps
- Enhancing data literacy
## Part I: Master the Basics
- Understanding correlation
- Definition and examples
- How correlation is measured
- Understanding causation
- Definition and examples
- Difference between correlation and causation
- Why people confuse correlation with causation
- Human tendency to find patterns
- Cognitive biases
## Part II: The Five Traps of Correlation vs. Causation
- Trap 1: Confusing coincidence with causation
- Random correlations
- Examples of coincidental relationships
- Trap 2: Overlooking confounding variables
- What are confounding variables?
- How they can create false correlations
- Trap 3: Reverse causation
- When cause and effect are reversed
- Identifying reverse causation
- Trap 4: Selection bias
- How biased samples lead to misleading conclusions
- Real-world examples of selection bias
- Trap 5: Post hoc fallacy
- Assuming causation because one event follows another
- Examples of post hoc reasoning
## Part III: Developing Critical Thinking
- Analyzing data critically
- Questioning assumptions
- Avoiding oversimplification
- Tools for critical thinking
- Logical reasoning
- Asking the right questions
- Visual examples to clarify concepts
- Graphs and charts
- Case studies
## Part IV: Practical Strategies for Identifying True Causal Relationships
- Formulating a plan to analyze data
- Steps to avoid falling into traps
- Structured approach to data analysis
- Testing for alternatives to causation
- Exploring other explanations for correlation
- Systematic testing methods
- Interpreting results accurately
- Avoiding overconfidence in conclusions
- Recognizing limitations in data
## Part V: Making Smarter, Data-Driven Decisions
- Applying what you’ve learned
- Real-life applications
- Examples from business, science, and everyday life
- Improving data literacy
- Building confidence in interpreting data
- Communicating findings effectively
- Final thoughts
- The importance of skepticism
- Continuous learning in data analysis