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Teaching the Difficult Past of Statistics to Improve the Future

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Abstract & Overview

In recent years, the discipline of statistics has begun reckoning with its difficult history. Institutions are reconsidering names that have honored key historical figures in statistics who have deep ties to eugenics movements and racial and class prejudice. These names, however, continue to appear in our classrooms, where we teach the methods created by these individuals, raising the question of how instructors should address their legacies. Three examples of famous statisticians and their work—Francis Galton’s use of conditional probabilities to demonstrate “hereditary talent,” Karl Pearson’s attempt to quantify the intelligence of Jewish immigrant students, and Ronald A. Fisher’s creation of the analysis of variance to de-emphasize environment in human development—highlight the intimate ties between statistics and eugenics. These examples, along with a discussion of the context of these men, eugenics movements, and the statisticians and scientists who opposed their eugenic programs, can humanize the field for students, teach them about the challenges in accurate and unbiased data collection and analysis, and connect historical mistakes to contemporary ethical issues. Confronting this history in the classroom can both improve the teaching of the statistical methodologies themselves and begin a broader conversation about the role of statistics in the world.

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Lee Kennedy‐Shaffer (2023). Teaching the Difficult Past of Statistics to Improve the Future. Journal of Statistics and Data Science Education, 32(1), 108–119. https://doi.org/10.1080/26939169.2023.2224407

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Kennedy‐Shaffer, L. (2023). Teaching the Difficult Past of Statistics to Improve the Future. Journal of Statistics and Data Science Education, 32(1), 108–119. https://doi.org/10.1080/26939169.2023.2224407
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