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Statistical Rethinking Mcelreath
A course that re-trains researchers to approach statistics as a principled process of building, comparing, and critiquing generative models within a Bayesian framework to achieve causal understanding and predictive accuracy.
A profile of this book is on the way.
What it’s about
For researchers uneasy with the traditional statistical cookbook of p-values and canned tests, 'Statistical Rethinking' offers a complete, hands-on course in modern Bayesian data analysis. It reframes statistical modeling as 'golem engineering,' a craft of building custom models from first principles to answer specific scientific questions. Using a code-intensive approach with R and Stan, the book guides readers from the fundamentals of probability as counting possibilities to the construction of sophisticated tools like multilevel models and causal inference with Directed Acyclic Graphs (DAGs). By emphasizing practical implementation, prior predictive simulation, and principled model comparison, it empowers researchers to not only use statistics, but to truly understand, justify, and critique their own analytical work.
The through-line
- Who it’s for
- A researcher in the natural or social sciences who has a basic understanding of regression but feels uneasy and unconfident about conventional statistical practices (p-values, a zoo of tests) and wants a more intuitive, unified, and powerful framework for statistical modeling.
- The problem
- Standard statistical toolboxes are inflexible, confusing, and often ill-suited for the specific and novel research contexts that modern researchers face, making it difficult to analyze complex data correctly. The researcher feels anxious about their statistical choices, fearing they are using the 'wrong' test, misinterpreting results, and lacking the ability to build the models they truly need to answer their questions.
- The plan
- Learn the fundamentals of Bayesian inference as a logical system of counting possibilities.
- Master building and interpreting a wide range of models (linear, GLM, multilevel) using an explicit, formula-based language.
- Employ formal tools for causal reasoning (DAGs) and model comparison (information criteria) to make principled analytical decisions.
- The payoff
- The researcher becomes a confident 'golem engineer,' able to design, build, and critique custom statistical models tailored to their specific research questions. · They can make more robust inferences, perform causal analyses with clarity, and transparently communicate their statistical assumptions and results. · They transform statistical anxiety into statistical wisdom, equipped with a powerful and flexible toolkit for modern scientific research.
See our guide
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Additional reading
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences · Cohen, J., & Cohen, P.
The book recommends this text for its excellent, accessible narrative discussions on regression, particularly for explanatory purposes in the social sciences.
- Classical and Modern Regression with Applications · Myers, R.
Cited as an excellent resource for its modern approach to regression analysis, especially its strong treatment of regression diagnostics for checking assumptions and identifying influential data points.
- Multivariate Statistical Methods in Behavioral Research · Bock, R. D.
This text is frequently cited by the author for more advanced or technical explanations of concepts in MANOVA, repeated measures, and step-down analysis.
- The Analysis of Covariance and Alternatives · Huitema, B.
Recommended as a very comprehensive and thorough text for readers wishing to gain a deeper understanding of Analysis of Covariance (ANCOVA).
- Structural Equations with Latent Variables · Bollen, K. A.
The guest-authored chapter on SEM heavily references this book as a key source for understanding fundamental concepts like model identification.
- Hierarchical Linear Models: Applications and Data Analysis Methods · Raudenbush, S., & Bryk, A.
The guest-authored chapter on Hierarchical Linear Modeling (HLM) cites this as the seminal text on the topic and the basis for the HLM software.
- Applied Discriminant Analysis · Huberty, C.
The chapter on Discriminant Analysis introduces this book as an excellent, current, and very thorough resource on the topic.
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan · John Kruschke
Recommended for reading 'During this book' to get additional information and a different perspective on Bayesian statistics and modeling.
- Regression and Other Stories · Andrew Gelman, Jennifer Hill, & Aki Vehtari
Recommended for reading 'During this book' as a supplementary text for a broader understanding of regression and Bayesian modeling.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan · Richard McElreath
Recommended for reading 'After this book' as a next step for readers who have mastered the concepts and wish to deepen their understanding of Bayesian modeling.