Recommended reading

• Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed.).
  Emphasizes conceptual understanding of estimation and uncertainty. Especially strong on interpreting standard errors and margins of error.

• Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the Practice of Statistics (9th ed.).
  A classic intro text that develops inference from sampling distributions through confidence intervals and practical interpretation.

• Bolstad, W. M. (2013). Introduction to Bayesian Statistics (3rd ed.). Wiley.
  A clear, methodical introduction that bridges classical and Bayesian inference. It covers conjugate priors, credible intervals, and basic hierarchical models, with R-based examples and exercises.

• Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis (2nd ed.). Springer-Verlag.
  A classic and comprehensive reference that develops Bayesian methods from first principles of decision theory. Recommended for readers who wish to explore the mathematical foundations in more depth.

• Nate Silver (2016). How FiveThirtyEight’s Election Forecast Works.
  FiveThirtyEight’s official explanation of the 2016 model, including poll weighting, correlated errors, and simulation methodology.
  https://fivethirtyeight.com/features/how-fivethirtyeights-election-forecast-works/

• FiveThirtyEight (2020). How Our Presidential Forecast Works (2020 Edition).
  Details updates to the 2020 model, including adjustments for state and national polling correlation, house effects, and uncertainty in turnout and the Electoral College.
  https://fivethirtyeight.com/features/how-our-presidential-forecast-works/

• Elliott Morris (2020). How to Build an Election Forecast Model.
  A comparative explanation of different forecasting approaches, including FiveThirtyEight’s, written for The Economist readers.
  https://projects.economist.com/us-2020-forecast/methodology

• Andrew Gelman (2020). Election Forecasting: Why We’re Not as Sure as We Think.
  A statistical perspective on model uncertainty, correlated polling errors, and the interpretation of forecast probabilities.
  https://statmodeling.stat.columbia.edu/2020/11/02/election-forecasting-why-were-not-as-sure-as-we-think/

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Springer.
  See the chapter on resampling methods for a clear, concise introduction to the bootstrap with intuitive examples and code.

• Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.
  The classic, authoritative treatment. Develops the bootstrap from first principles, with theory, practical guidance, and many examples.