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The perils of policy by p-value: Predicting civil conflicts


  • Michael D Ward

    (Department of Political Science, Duke University)

  • Brian D Greenhill

    (Department of Political Science, University of Washington)

  • Kristin M Bakke

    (Department of Political Science, University College London)


Large-n studies of conflict have produced a large number of statistically significant results but little accurate guidance in terms of anticipating the onset of conflict. The authors argue that too much attention has been paid to finding statistically significant relationships, while too little attention has been paid to finding variables that improve our ability to predict civil wars. The result can be a distorted view of what matters most to the onset of conflict. Although these models may not be intended to be predictive models, prescriptions based on these models are generally based on statistical significance, and the predictive attributes of the underlying models are generally ignored. These predictions should not be ignored, but rather need to be heuristically evaluated because they may shed light on the veracity of the models. In this study, the authors conduct a side-by-side comparison of the statistical significance and predictive power of the different variables used in two of the most influential models of civil war. The results provide a clear demonstration of how potentially misleading the traditional focus on statistical significance can be. Until out-of-sample heuristics - especially including predictions - are part of the normal evaluative tools in conflict research, we are unlikely to make sufficient theoretical progress beyond broad statements that point to GDP per capita and population as the major causal factors accounting for civil war onset.

Suggested Citation

  • Michael D Ward & Brian D Greenhill & Kristin M Bakke, 2010. "The perils of policy by p-value: Predicting civil conflicts," Journal of Peace Research, Peace Research Institute Oslo, vol. 47(4), pages 363-375, July.
  • Handle: RePEc:sae:joupea:v:47:y:2010:i:4:p:363-375

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    Cited by:

    1. Leiter, Debra & Murr, Andreas & Rascón Ramírez, Ericka & Stegmaier, Mary, 2018. "Social networks and citizen election forecasting: The more friends the better," International Journal of Forecasting, Elsevier, vol. 34(2), pages 235-248.
    2. Stijn van Weezel, 2016. "Short term effects of drought on communal conflict in Nigeria," Working Papers 201618, School of Economics, University College Dublin.
    3. Ruhe, Constantin, 2012. "Predicting atrocities. Statistically modeling violence against civilians during civil war," NEPS Working Papers 7/2012, Network of European Peace Scientists.
    4. Beger, Andreas & Dorff, Cassy L. & Ward, Michael D., 2016. "Irregular leadership changes in 2014: Forecasts using ensemble, split-population duration models," International Journal of Forecasting, Elsevier, vol. 32(1), pages 98-111.
    5. Hannes Mueller & Christopher Rauh, 2022. "The Hard Problem of Prediction for Conflict Prevention," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
    6. Brandt, Patrick T. & Freeman, John R. & Schrodt, Philip A., 2014. "Evaluating forecasts of political conflict dynamics," International Journal of Forecasting, Elsevier, vol. 30(4), pages 944-962.
    7. Thomas Bernauer & Tobias Bohmelt, 2014. "Basins at Risk: Predicting International River Basin Conflict and Cooperation," Global Environmental Politics, MIT Press, vol. 14(4), pages 116-138, November.
    8. Joshi, Devin K. & Hughes, Barry B. & Sisk, Timothy D., 2015. "Improving Governance for the Post-2015 Sustainable Development Goals: Scenario Forecasting the Next 50years," World Development, Elsevier, vol. 70(C), pages 286-302.
    9. Trude Midtgaard & Krishna Vadlamannati & Indra Soysa, 2014. "Does the IMF cause civil war? A comment," The Review of International Organizations, Springer, vol. 9(1), pages 107-124, March.
    10. Gerdis Wischnath & Halvard Buhaug, 2014. "On climate variability and civil war in Asia," Climatic Change, Springer, vol. 122(4), pages 709-721, February.
    11. Freire, Danilo & Uzonyi, Gary, 2018. "What Drives State-Sponsored Violence?: Evidence from Extreme Bounds Analysis and Ensemble Learning Models," SocArXiv pzx3q, Center for Open Science.
    12. Montgomery, Jacob M. & Hollenbach, Florian M. & Ward, Michael D., 2015. "Calibrating ensemble forecasting models with sparse data in the social sciences," International Journal of Forecasting, Elsevier, vol. 31(3), pages 930-942.
    13. Ilya Lokshin, 2015. "Whatever Explains Whatever: The Duhem-Quine Thesis And Conventional Quantitative Methods In Political Science," HSE Working papers WP BRP 23/PS/2015, National Research University Higher School of Economics.
    14. Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2019. "Infinitely Stochastic Micro Forecasting," Papers 1908.10636,, revised Sep 2019.
    15. Murr, Andreas E., 2015. "The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 916-929.


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