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References for Causation and Study Design

Curated resources to deepen your understanding of causation, confounding, and the differences between observational studies and experiments.

Wikipedia Articles

  1. Correlation does not imply causation - Wikipedia - Classic explanation of why statistical association between variables does not prove that one causes the other, with memorable examples.

  2. Confounding - Wikipedia - Detailed coverage of confounding variables, how they distort apparent relationships, and methods researchers use to control for them.

  3. Simpson's paradox - Wikipedia - Explains how trends appearing in subgroups can reverse when data is combined, with famous real-world examples from medicine and admissions.

Textbooks

  1. The Practice of Statistics by Starnes, Tabor, Yates, and Moore - W.H. Freeman (2018) - Thorough coverage of observational studies versus experiments, random assignment, and establishing causation for the AP exam.

  2. Statistics Through Applications by Yates, Moore, and Starnes - W.H. Freeman (2008) - Accessible introduction to study design with real-world case studies showing how confounding affects research conclusions.

Online Resources

  1. Experiments vs Observational Studies - Khan Academy - Clear video explanations of the difference between study types and why only experiments with randomization can establish causation.

  2. Causal Inference in Statistics: A Primer - Judea Pearl (online excerpts) - Introduction to modern thinking about causation, including causal diagrams that help visualize confounding relationships.

  3. Understanding Simpson's Paradox - Brilliant.org - Interactive lessons walking through examples of Simpson's Paradox and how to recognize when aggregated data might be misleading.

  4. Randomized Controlled Trials Explained - NIH National Library of Medicine - Overview of how medical researchers design experiments to establish whether treatments actually work.

  5. AP Statistics: Data Collection - College Board - Official course framework and sample questions covering observational studies, experiments, and confounding for exam preparation.