History of statistics
Last updated: 2024-04-05
This course may be taken as a reading course in DoSS or Information. Please get in touch if interested.
Overview
Statistics and the data sciences have a long and robust history. Understanding that history provides students with a better appreciation for the methods that they are applying today.
Often students are taught, say, linear regression in such a way that they come to believe that statisticians simply stumbled upon it one day. In fact, the idea of combining different observations in this way, took the work of decades and even centuries to come to terms with. Understanding the history of statistics and data sciences, more generally, provides a more solid foundation for applying those skills today. We are interested in why certain methods were developed, and became popular, and the circumstances under which this occurred because that provides us with a nuanced knowledge of when we should apply them ourselves.
We study history because we want to understand how our predecessors solved their problems. That means understanding, not just what they did, but the circumstances in which they did it, and the choices they faced. That knowledge allows us to better solve our own problems. At the very least, it helps us to avoid repeating mistakes; and, if fully accomplished, can even allow to improve our own approaches.
The history of statistics and the data sciences is one of greatness, and we will cover that extensively. But it also one in which that greatness was sometimes developed for abhorrent purposes, and there were many contributors, actual or potential, who were overlooked. We will cover these aspects too.
The hope is that having taken this course, you will understand what you have been studying in statistics and the data sciences with fresh eyes, and bring this deeper appreciation with you throughout the rest of your career.
Learning objectives
The purpose of the course is to develop an appreciation of history of statistics and the data sciences to such an extent so as to provide a firmer foundation for your conduct of applied statistics and data science. By the end of the course, you should be able to:
- Engage critically with ideas and readings in the history of statistics and data sciences.
- Conduct research in the history of data science and statistics.
- Write and present your research.
- Understand why the methods and approaches developed when they did, and the circumstances under which they developed.
- Appreciate that much of the statistical machinery that we use today was developed with respect to eugenics.
- Respectfully identify strengths and weaknesses in the work of others.
- Reflect effectively on your own learning and professional development.
Content
Week 1 "Overview"
Early astronomical and gambling underpinnings. Least squares, combining observations, and uncertainty. Legendre, Laplace, Bernoulli, De Moivre, Simpson.
- Fienberg, Stephen E., 1992, "A Brief History of Statistics in Three and One-Half Chapters: A Review Essay", Statistical Science, 7 (2), 10.1214/ss/1177011360.
- Kendall, Maurice, 1960, "Studies in the history of probability and statistics: Part X. Where shall the history of statistics begin?", Biometrika, 47 (3-4), 10.1093/biomet/47.3-4.447, pp. 447-449.
- Stigler, Stephen, 1986, The History of Statistics, Belknap Harvard. Chapters 1-2.
- Hacking, Ian, 2006, The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference, Cambridge University Press. Chapter "Introduction".
Week 2 "The 1700s"
Inverse probability. Gauss, Laplace, Central Limit Theorem.
- Stigler, Stephen, 1986, The History of Statistics, Belknap Harvard. Chapters 3-4.
- Sheynin, Oscar, 2018, "Theory of Probability. A Historical Essay", arXiv, 1802.09966. Chapters 1-7.
- Miller, Joshua B. and Andrew Gelman, 2020, Laplace's Theories of Cognitive Illusions, Heuristics and Biases", Statistical Science, 35 (2), 10.1214/19-sts696
- Kahneman, Daniel and Maya Bar-Hillel, 2020, "Comment: Laplace and Cognitive Illusions", Statistical Science, 35 (2), 10.1214/19-sts750.
- Shafer, Glenn, 2020, "Comment: Illusions, Then and Now", Statistical Science, 35 (2), 10.1214/19-sts751.
- Miller, Joshua B. and Andrew Gelman, 2020, "Rejoinder: Laplace's theories of cognitive illusions, heuristics and biases", Statistical Science, 35 (2), 10.1214/20-sts779.
Week 3 "Early 1800s"
Adoption by the social sciences. Quetelet, Poisson, Cournot, Lexis, binomials and Law of Large Numbers.
- Stigler, Stephen, 1986, The History of Statistics, Belknap Harvard. Chapters 5-6.
- Sheynin, Oscar, 2018, "Theory of Probability. A Historical Essay", arXiv, 1802.09966. Chapters 8-9
Week 4 "Late 1800s"
Adoption by eugenics. Galton, Edgeworth, and Pearson. Regression and correlation.
- Stigler, Stephen, 1986, The History of Statistics, Belknap Harvard. Chapters 7-8.
- Sheynin, Oscar, 2018, "Theory of Probability. A Historical Essay", arXiv, https://arxiv.org/abs/1802.09966. Chapters 10-11
- Salsburg, David, 2002, The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century, Henry Holt and Company.
- Porter, Theodore, 2020, The Rise of Statistical Thinking, 1820–1900, Princeton University Press.
Week 5 "Early 1900s"
Edgeworth, Pearson, and Yule. Regression, and correlation.
- Stigler, Stephen, 1986, The History of Statistics, Belknap Harvard. Chapters 9-10.
- David Freedman, 1999, "From association to causation: some remarks on the history of statistics", Statistical Science, 14 (3), pp. 243-258.
- Donald MacKenzie, 1981, Statistics in Britain, 1865-1930: The Social Construction of Scientific Knowledge.
- Erich Lehmann, 2011, Fisher, Neyman, and the Creation of Classical Statistics, Springer.
Week 6 "Early 1900s (cont.)"
- Stigler, Stephen, 1996, 'The History of Statistics in 1933', Statistical Science
- Sheynin, Oscar, 2018, "Theory of Probability. A Historical Essay", arXiv, 1802.09966. Chapters 12-15
- Stigler, Stephen, 2016, The Seven Pillars of Statistical Wisdom, Harvard University Press.
- von Plato, Jan, 1994, Creating Modern Probability, Cambridge University Press.
- Lehmann, Erich, 2007, Reminiscences of a Statistician: The Company I Kept, Springer.
Week 7 "Data visualization"
- Friendly, Michael, and Howard Wainer, 2021, A History of Data Visualization and Graphic Communication, Harvard University Press. Chapters 1-6 and 9
Week 8 "Bayesian methods"
- Fienberg, Stephen E., 2003, "When Did Bayesian Inference Become 'Bayesian'?", Bayesian Analysis, 1 (1), 10.1214/06-BA101, pp. 1-37.
- Leonard, Thomas Hoskyns, 2014, "A personal history of Bayesian statistics", WIREs Computational Statistics, 6 (2), 10.1002/wics.1293, pp. 80-115.
- McGrayne, Sharon Bertsch, 2012, The Theory That Would Not Die, Yale University Press.
- Nordhaus, William D., 2007, "Two Centuries of Productivity Growth in Computing", The Journal of Economic History, 67 (1), 10.1017/s0022050707000058, pp. 128–159.
- Lindley, Dennis V., 2000, "The Philosophy of Statistics", Journal of the Royal Statistical Society: Series D (The Statistician), 49 (3), 10.1111/1467-9884.00238, pp. 293-337.
- Gelman, Andrew and Christian P. Robert, 2013, "Not only defended but also applied: The perceived absurdity of Bayesian inference", American Statistician, 67 (1), 10.1080/00031305.2013.760987, pp. 1-5.
- Gelman, Andrew and Christian P. Robert, 2013, "Rejoinder: The Anti-Bayesian Moment and Its Passing", The American Statistician, 67 (1), 10.1080/00031305.2012.752409, pp. 16–17.
Week 9 "Causal inference"
- Pearl, Judea and Dana Mackenzie, 2018, The Book of Why: The New Science of Cause and Effect, Basic Books. Chapter 2.
Week 10 "Whither statistics? The rise of data science"
- Breiman, Leo, 2001, "Statistical Modeling: The Two Cultures", Statistical Sciences.
- Hand, David J., 2015, "Statistics and computing: the genesis of data science", Statistics and Computing.
- Donoho, David, 2017, "50 Years of Data Science", Journal of Computational and Graphical Statistics.
- Gelman, Andrew and Aki Vehtari, 2021,"What are the Most Important Statistical Ideas of the Past 50 Years?", Journal of the American Statistical Association, 116 (536), 10.1080/01621459.2021.1938081, pp. 2087–2097.
Week 11 "Overlooked contributors"
- Anderson, Margo, 1992, "The History of Women and the History of Statistics", Journal of Women's History
- Hafner, Katie, 2021, "Arianna Rosenbluth Dies at 93; Pioneering Figure in Data Science", The New York Times.
- Kitagawa–Blinder–Oaxaca decomposition.
- Thompson, Mary E., "Reflections on women in statistics in Canada", in Xihong Lin, et al., eds, 2014, Past, present, and future of statistical science, CRC Press.
- Reid, Nancy M., "The whole women thing", in Xihong Lin, et al., eds, 2014, Past, present, and future of statistical science, CRC Press.
- Ryan, Louise M., "Reflections on diversity", in Xihong Lin, et al., eds, 2014, Past, present, and future of statistical science, CRC Press.
Week 12 "Reckoning with the past and thinking about the future: Statistics and society"
- Desrosières, Alain, 2002, The Politics of Large Numbers, Harvard University Press.
- Lin, Xihong, et al., eds, 2014, Past, present, and future of statistical science, CRC Press.
Assessment
Tutorial papers
- Due dates: Fortnightly during the lecture time in Weeks 3, 5, 7, 9, and 11.
- Weight: Each is worth 15 per cent; the worst one is dropped.
- Task: Write a paper in the first hour of class of 2-6 pages on a topic covered in the preceding two weeks. You will be given a choice of questions and expected to pick one. Your paper should be neatly written, well-organized, and referenced. These will then be circulated and discussed in class.
Final Paper
- Due date: Final day of exam block.
- Weight: 40 per cent.
- Task: Write an original paper on a topic covered in the class and submit it to a journal and do not get desk-rejected.