Why inferential statistics are inappropriate for development studies and how the same data can be better used
The purpose of this paper is twofold: 1) to highlight the widely ignored but fundamental problem of ‘superpopulations’ for the use of inferential statistics in development studies. We do not to dwell on this problem however as it has been sufficiently discussed in older papers by statisticians that social scientists have nevertheless long chosen to ignore; the interested reader can turn to those for greater detail. 2) to show that descriptive statistics both avoid the problem of superpopulations and can be a powerful tool when used correctly. A few examples are provided. The paper ends with considerations of some reasons we think are behind the adherence to methods that are known to be inapplicable to many of the types of questions asked in development studies yet still widely practiced.
|Date of creation:||06 Jan 2011|
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- Hoover,Kevin D., 2001.
"Causality in Macroeconomics,"
Cambridge University Press, number 9780521002882, March.
- Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521452175, March.
- Judea Pearl, 2003. "Statistics and causal inference: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(2), pages 281-345, December.
- repec:cup:apsrev:v:88:y:1994:i:02:p:412-423_09 is not listed on IDEAS
- David A. Freedman & Stephen P. Klein & Jerome Sacks & Charles A. Smyth & Charles G. Everett, 1991. "Rejoinder," Evaluation Review, , vol. 15(6), pages 800-816, December.
- Hall, Peter A. & Franzese, Robert J., 1998. "Mixed Signals: Central Bank Independence, Coordinated Wage Bargaining, and European Monetary Union," International Organization, Cambridge University Press, vol. 52(03), pages 505-535, June. Full references (including those not matched with items on IDEAS)
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