Ⅰ.
Basic understanding of statistics and errors
Why You Should Study Errors - Essential Insights in a Data Society
The Illusion of Statistics: The Duality of Belief and Distrust in Numbers
The Four Fallacies: Bias, Error, Mistake, and Interpretation
Statistical Thinking: Finding Order in Uncertainty
The Probabilistic World: Human Skills in Dealing with Uncertainty
The Power of Numbers: Why Data Appears to Be Truth
The Psychology of Illusion: Traps Researchers and Readers Fall Into
Statistics and Society - The Power That Drives Science, Media, and Policy
The Repercussions of Error: The Social Costs of Misinterpretation
A skeptical attitude: trusting statistics while remaining wary.

Ⅱ.
The Many Faces of Bias and Distortion
Sampling Bias - The Risk of Representative Results
Measurement bias - distortions arising from questions and instruments
Researcher Bias - An Illusion Created by Observer Expectations
Publication Bias: The Structure of Science Where Only Positive Results Survive
Social desirability bias - false responses created by face
Self-selection bias - distortions created by those who participate voluntarily.
Questionnaire Design Bias - The Traps That Question Structure Creates in Response
Respondent fatigue bias - the error caused by repeated surveys.
Data Collection Bias - The Illusion of Omission and Imbalance
Big Data Bias: The Invisible Bias in Massive Data
Algorithmic Bias: Social Inequality Reproduced by AI
Policy Bias - Misjudgments Created by Intentional Indicator Selection

Ⅲ.
Statistical error and human error
Sampling error - large uncertainty created by a small sample
Nonsampling error - errors arising from design and implementation
Type I error - the illusion of attributing a nonexistent effect
Type II Error - Missing the Real Effect
Measurement error - discrepancies revealed in repeated measurements
Model Specification Errors - Studies Falling into the Variable Selection Trap
Violation of assumptions - breakdown of normality, homoscedasticity, and independence
Data processing errors - errors that occur during input and cleaning.
Coding Errors - Common Pitfalls in Python and R
Analysis procedure error - Missing values ​​and multiple comparisons failed to be handled.

Ⅳ.
Interpretation errors and numerical errors
Confusing Correlation and Causation: The Third Variable and the Pitfalls of Causal Inference
Statistical and Practical Significance - Understanding Effect Size Beyond P-Values
Overgeneralization - the error of extending partial results to the whole.
Data snooping - selecting only favorable results from numerous analyses.
p-hacking - The Researcher's Temptation to Force Significance
The illusion of causality: interpreting cause and effect backwards.
Graph Interpretation Errors - The Illusions of Visualization
The Average Trap: The Distortion of Numbers Driven by Extreme Values
Ignoring the median and distribution - an optical illusion that emphasizes only the mean
Ignoring interactions - reducing multidimensional relationships to a single effect.
Overestimation of small effects - the illusion of statistical significance
Underestimating large effects - the error of ignoring real influence

V.
Real-Life Errors and Future Challenges
Media Reporting Errors - Exaggerated Figures and Misinterpretations
Policy Evaluation Errors: The Traps of Education and Health Indicators
Corporate Marketing Mistakes: Averages and Selective Reporting in Advertising
Error Prevention Strategies - Checklist Procedures Researchers Should Follow
Reproducibility and Research Ethics: The Keys to Honest Data Science
Statistical Thinking in the AI ​​Age: Beyond Algorithmic Bias

Ⅵ.
The People Behind the Numbers (Hypothetical Case 1)
The Emotional Gap: Can Happiness Be Defined in Numbers?
The Psychology of Risk: Why Fear Is More Powerful Than Probability
The Blind Spot of Statistics: The Individual Stories Obscured by Averages
Intuition and Data - Choices That Were Right by Feeling
The Weight of Trust: Can We Trust People More Than Data?
Statistics and Morality - Right Numbers, Wrong Judgments
The Atypical Truth: The Exceptions Statistics Can't Read
The Man in the Numbers - The Courage to Embrace Uncertainty
The Language of Graphs - When Visualization Becomes Manipulative
Data Justice - A New Standard for Fairness and Transparency

Ⅶ.
The Philosophy of Uncertainty (Hypothetical Case 2)
The Limits of Probability: Why the World Is Unpredictable
Statistical Imagination - Seeing Beyond Data
The Power of Narrative - Human Language Gives Meaning to Numbers
The Frontiers of Academics - Where Statistics, Philosophy, and Art Meet
The Aesthetics of Doubt - Freedom in Uncertainty
Between Belief and Delusion: The Final Question Left by Statistics