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

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 atte...

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Published inJournal of peace research Vol. 47; no. 4; pp. 363 - 375
Main Authors Ward, Michael D, Greenhill, Brian D, Bakke, Kristin M
Format Journal Article
LanguageEnglish
Published London, England Sage Publications 01.07.2010
SAGE Publications
Sage Publications Ltd
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Abstract 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.
AbstractList 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.
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. Reprinted by permission of Sage Publications Ltd
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. [Reprinted by permission of Sage Publications Ltd., copyright holder.]
Author Bakke, Kristin M
Ward, Michael D
Greenhill, Brian D
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Posen, Barry 1993; 35
Lake, David A, Donald Rothchild 1996; 21
O’Brien, Sean P 2002; 46
Gill, Jeff 1999; 52
Hegre, Håvard, Nicholas Sambanis 2006; 50
Bass, Gary J 2006
Collier, Paul, Anke Hoeffler, Måns Söderbom 2008; 45
Collier, Paul, Anke Hoeffler 2004; 56
Fearon, James D, David D Laitin 2003; 97
Lacina, Bethany, Nils Petter Gleditsch 2005; 21
Gurr, Ted Robert, Mark Irving Lichbach 1986; 19
Hewstone, Miles, Katy Greenland 2000; 35
Schrodt, Philip A, Deborah J Gerner 2000; 94
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Hegre, Håvard (atypb24) 2006; 50
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Bass, Gary J (atypb2) 2006
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atypb25
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Easterly, William (atypb12) 2008
Varshney, Ashutosh (atypb44) 2002
Horowitz, Donald (atypb26) 1985
Raleigh, Clionadh (atypb38)
Gurr, Ted Robert (atypb21) 1970
Schrodt, Philip A (atypb40)
atypb15
atypb37
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Collier, Paul (atypb6) 2007
atypb13
Gurr, Ted Robert (atypb22) 2000
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Diamond, Larry (atypb11) 2006
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Lemann, Nicholas (atypb34) 2001
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Kaplan, Robert D (atypb29) 1993
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Snippet Large-n studies of conflict have produced a large number of statistically significant results but little accurate guidance in terms of anticipating the onset...
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SubjectTerms Accounting
Attention
Civil War
Civil wars
Commodities
Comparative analysis
Conflict
Conflict theory
Cross-validation
Democracy
Ethnic conflict
False positive errors
Forecasts
GDP
Gross Domestic Product
Heuristic
Heuristics
Model testing
Modeling
Per capita output
Population dynamics
Power
Prediction
Prediction models
Predictive modeling
Predictive power
Prescription drugs
Significance tests
Statistical models
Statistical Significance
Variables
Title The perils of policy by p-value: Predicting civil conflicts
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