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“All Models Are Wrong”

Published online by Cambridge University Press:  23 October 2020

Extract

Several years ago, the first thing i learned in my introductory statistics class was the following declaration, which the instructor had written in capital letters on the blackboard: “all models are wrong.” Models are statistical, graphic, or physical objects, and their primary quality is that they can be manipulated. Scientists and social scientists use them to think about the social or natural worlds and to represent those worlds in a simplified manner. Statistical models, which dominate the social sciences, particularly in economics, are typically equations with response and predictor variables. Specifically, a researcher seeks to understand some social phenomenon, such as the relation between students' scores on a math test and how many hours the students spent preparing for the exam. To predict or describe this relation, the researcher constructs a quantitative model with quantitative inputs (the number of hours each student spent studying) and outputs (each student's test score). The researcher hopes that the number of hours a student spent preparing for the exam will correlate with the student's score. If it does, this quantified relation can help describe the overall dynamics of test taking.

Type
Theories and Methodologies
Copyright
Copyright © 2017 The Modern Language Association of America

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References

Works Cited

Allison, Sarah, et al. “Style at the Scale of the Sentence.” Stanford Literary Lab, pamphlet 5, June 2013, litlab.stanford.edu/pamphlets/. PDF download.Google Scholar
Box, George E. P.Robustness in the Strategy of Scientific Model Building.” Robustness in Statistics, edited by Launer, R. L. and Wilkinson, G. N., Academic Press, 1976, pp. 201–36.Google Scholar
English, James F.Morettian Picaresque.” Franco Moretti's Distant Reading: A Symposium, 27 June 2013. Los Angeles Review of Books, lareviewoftooks.org/article/franco-morettis-distant-reading-a-symposium.Google Scholar
Fitzpatrick, Kathleen. “The Ends of Big Data.” Franco Moretti's Distant Reading: A Symposium, 27 June 2013. Los Angeles Review of Books, lareviewofbooks.org/article/franco-morettis-distant-reading-a-symposium.Google Scholar
Freedman, Jonathan. “After Close Reading.” The New Rambler, 13 Apr. 2015, newramblerreview.com/book-reviews/literary-studies/after-close-reading.Google Scholar
Galloway, Alexander R.Everything Is Computation.” Franco Moretti's Distant Reading: A Symposium, 27 June 2013. Los Angeles Review of Books, lareviewoftooks.org/article/franco-morettis-distant-reading-a-symposium.Google Scholar
Lerer, Seth. Error and the Academic Self. Columbia UP, 2002.Google Scholar
Long, Hoyt, and So, Richard Jean. “Literary Pattern Recognition: Modernism between Close Reading and Machine Learning.” Critical Inquiry, vol. 42, no. 2, Winter 2016, pp. 235–67.CrossRefGoogle Scholar
MacKenzie, Donald. An Engine, Not a Camera: How Financial Models Shape Markets. MIT P, 2008.Google Scholar
Moretti, Franco. Distant Reading. Verso, 2013.Google Scholar
Morgan, Mary. The World in the Model: How Economists Work and Think. Cambridge UP, 2012.CrossRefGoogle Scholar
Poovey, Mary. “On ‘the Limits to Financialization.‘Dialogues in Human Geography, 10 July 2015, journals.sagepub.com/toc/dhga/5/2.CrossRefGoogle Scholar
Wimsatt, W. C. Re-engineering Philosophy for Limited Beings: Piecewise Approximations to Reality. Harvard UP, 2007.Google Scholar