# Book: Statistical Thinking for the 21st Century (Poldrack)

- Page ID
- 7637

Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge. The foundations of statistical thinking come primarily from mathematics and statistics, but also from computer science, psychology, and other fields of study.

- Front Matter
- 1: Introduction
- 2: Working with Data
- 3: Introduction to R
- 4: Summarizing Data
- 5: Summarizing Data with R (with Lucy King)
- 6: Data Visualization
- 7: Data Visualization with R (with Anna Khazenzon)
- 8: Fitting Models to Data
- 9: Fitting Simple Models with R
- 10: Probability
- 11: Probability in R (with Lucy King)
- 12: Sampling
- 13: Sampling in R
- 14: Resampling and Simulation
- 15: Resampling and Simulation in R
- 16: Hypothesis Testing
- 17: Hypothesis Testing in R
- 18: Quantifying Effects and Desiging Studies
- 19: Statistical Power in R
- 20: Bayesian Statistics
- 21: Bayesian Statistics in R
- 22: Modeling Categorical Relationships
- 23: Modeling Categorical Relationships in R
- 24: Modeling Continuous Relationships
- 25: Modeling Continuous Relationships in R
- 26: The General Linear Model
- 27: The General Linear Model in R
- 28: Comparing Means
- 29: Comparing Means in R
- 30: Practical statistical modeling
- 31: Practical Statistical Modeling in R
- 32: Doing Reproducible Research
- 33: References
- Back Matter