This page mostly exists to collect a good selection of overview statistics introductions that are not terrible.
I’m especially interested in modern fusion methods that harmonise what we would call *statistics* and *machine learning* methods, and the unnecessary terminological confusion between those systems.

Here are some recommended courses to get started if you don’t know what you’re doing.

See also the recommended texts below. May I draw your attention especially to Kroese et al. (2019), which I proof-read for my supervisor Zdravko Botev, and enjoyed greatly? It smoothly bridges non-statistics mathematicians into applied statistics, without being excruciating, unlike layperson introductions.

There are also statistics podcasts.

## References

Cox, D. R., and D. V. Hinkley. 2000.

*Theoretical Statistics*. Boca Raton: Chapman & Hall/CRC.
Dadkhah, Kamran. 2011.

*Foundations of Mathematical and Computational Economics*.
Devroye, Luc, László Györfi, and Gábor Lugosi. 1996.

*A Probabilistic Theory of Pattern Recognition*. New York: Springer. http://www.szit.bme.hu/ gyorfi/pbook.pdf.
Efron, Bradley, and Trevor Hastie. 2016.

*Computer Age Statistical Inference: Algorithms, Evidence, and Data Science*. Institute of Mathematical Statistics Monographs. New York, NY: Cambridge University Press.
Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013.

*Bayesian Data Analysis*. 3 edition. Chapman & Hall/CRC Texts in Statistical Science. Boca Raton: Chapman and Hall/CRC.
Guttman, Louis. 1977. “What Is Not What in Statistics.”

*Journal of the Royal Statistical Society. Series D (The Statistician)*26 (2): 81–107. https://doi.org/10.2307/2987957.
Guttorp, Peter. 1995.

*Stochastic Modeling of Scientific Data*. 1. ed. Stochastic Modeling Series. London: Chapman & Hall.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009.

*The Elements of Statistical Learning: Data Mining, Inference and Prediction*. Springer.
Kobayashi, Hisashi, Brian L. Mark, and William Turin. 2011.

*Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance*. Cambridge University Press.
Kroese, Dirk P., Zdravko I. Botev, Thomas Taimre, and Radislav Vaisman. 2019.

*Mathematical and Statistical Methods for Data Science and Machine Learning*. First edition. Chapman & Hall/CRC Machine Learning & Pattern Recognition. Boca Raton: CRC Press.
Lehmann, E. L., and George Casella. 1998.

*Theory of Point Estimation*. 2nd ed. Springer Texts in Statistics. New York: Springer.
Lehmann, Erich L., and Joseph P. Romano. 2010.

*Testing Statistical Hypotheses*. 3. ed. Springer Texts in Statistics. New York, NY: Springer.
Mohri, Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. 2018.

*Foundations of Machine Learning*. Second edition. Adaptive Computation and Machine Learning. Cambridge, Massachusetts: The MIT Press.
Murphy, Kevin P. 2012.

*Machine Learning: A Probabilistic Perspective*. 1 edition. Adaptive Computation and Machine Learning Series. Cambridge, MA: MIT Press.
Robert, Christian P., and George Casella. 2004.

*Monte Carlo Statistical Methods*. 2nd ed. Springer Texts in Statistics. New York: Springer.
Schervish, Mark J. 2012.

*Theory of Statistics*. Springer Series in Statistics. New York, NY: Springer Science & Business Media. https://doi.org/10.1007/978-1-4612-4250-5_1.
Vaart, Aad W. van der. 2007.

*Asymptotic Statistics*. 1. paperback ed., 8. printing. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge: Cambridge Univ. Press.
Wasserman, Larry. 2013.

*All of Statistics: A Concise Course in Statistical Inference*. Springer.