References
J. K. Blitzstein and J. Hwang. Introduction to Probability, 2nd edition. Chapman & Hall/CRC Press, 2019.
D. R. Brillinger. “… how wonderful the field of statistics is …” In Past, Present, and Future of Statistical Science, edited by X. Lin, C. Genest, et al., pp. 41–47. Chapman & Hall/CRC Press, 2014.
L. D’Agostino McGowan, T. Gerke, and M. Barrett. Causal inference is not just a statistics problem. Journal of Statistics and Data Science Education 32:2 (2024), 150–155.
C. D’Ignazio and L. F. Klein. Data Feminism. MIT Press, 2020.
V. Eubanks. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press, 2018.
M. P. Fox, K. Carr, et al. Will podcasting and social media replace journals and traditional science communication? No, but…. American Journal of Epidemiology 190:8 (2021), 1625–1631.
M. A. Hernán and J. M. Robins. Causal Inference: What If. Chapman & Hall/CRC Press, 2023.
N. Huntington-Klein. The Effect: An Introduction to Research Design and Causality. Chapman & Hall/CRC Press, 2022.
G. W. Imbens and D. B. Rubin. Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press, 2015.
L. Mlodinow. The Drunkard’s Walk: How Randomness Rules Our Lives. Pantheon Books, 2008.
S. U. Noble. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, 2018.
C. O’Neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, 2017.
J. A. Paulos. Innumeracy: Mathematical Illiteracy and Its Consequences. Hill and Wang, 1988.
J. Pearl and D. Mackenzie. The Book of Why: The New Science of Cause and Effect. Basic Books, 2020.
H. Schellmann. The Algorithm: How AI Decides Who Gets Hired, Monitored, Promoted, and Fired and Why We Need to Fight Back Now. Hachette, 2024.
N. Silver. The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t. Penguin Press, 2012.
D. Spiegelhalter. The Art of Statistics: How to Learn from Data. Basic Books, 2021.
S. Wachter-Boettcher. Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech. W. W. Norton & Company, 2017.
R. Wasserstein. George Box: a model statistician. Significance 7:3 (2010), 134–135.
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Downey connects the varied examples to a larger point, one that is a cornerstone of statistics and probability courses: ‘when the world deviates from the model, that’s a problem for the model, not a deficiency of the world.’
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Kennedy-Shaffer, L. Probably Overthinking It. Math Intelligencer (2024). https://doi.org/10.1007/s00283-024-10349-y
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DOI: https://doi.org/10.1007/s00283-024-10349-y