How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science
Gary King. 1986.
"How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science".
American Journal of Political Science, 30, Pp. 666–687.

Abstract
This article identifies a set of serious theoretical mistakes appearing with troublingly high frequency throughout the quantitative political science literature. These mistakes are all based on faulty statistical theory or on erroneous statistical analysis. Through algebraic and interpretive proofs, some of the most commonly made mistakes are explicated and illustrated. The theoretical problem underlying each is highlighted, and suggested solutions are provided throughout. It is argued that closer attention to these problems and solutions will result in more reliable quantitative analyses and more useful theoretical contributions.
See Also
- [Book] Numerical Issues Involved in Inverting Hessian Matrices (2003)
- [Paper] On Political Methodology (1991)
- [Book] The Changing Evidence Base of Social Science Research (2009)
- [Paper] What to Do When Your Hessian Is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation (2004)
- [Paper] Comment on 'Estimating the Reproducibility of Psychological Science' (2016)
- [Paper] If a Statistical Model Predicts That Common Events Should Occur Only Once in 10,000 Elections, Maybe It's the Wrong Model (2025)
- [Paper] The Science of Political Science Graduate Admissions (1993)
- [Presentation] Empowering Social Science Research With Industry Partnerships (Dean's Symposium on Social Science Innovations, Harvard) (2021)