by Summary:
Stripping a phemenon of its social context, normalizing a base rate to 50%, and seeking an on-off decision: all of these can give the feel of scientific objectivity—but the very steps taken to ensure objectivity can remove social context and relevance. Statistical Modeling, Causal Inference, and Social ScienceGaydar and the fallacy of objective measurementAndrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University
Topics:
Mike Norman considers the following as important: causal inference, experimental design, scientific objectivity, social science, statistical reasoning, statistics
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Stripping a phemenon of its social context, normalizing a base rate to 50%, and seeking an on-off decision: all of these can give the feel of scientific objectivity—but the very steps taken to ensure objectivity can remove social context and relevance. Statistical Modeling, Causal Inference, and Social ScienceGaydar and the fallacy of objective measurementAndrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University
Topics:
Mike Norman considers the following as important: causal inference, experimental design, scientific objectivity, social science, statistical reasoning, statistics
This could be interesting, too:
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Stripping a phemenon of its social context, normalizing a base rate to 50%, and seeking an on-off decision: all of these can give the feel of scientific objectivity—but the very steps taken to ensure objectivity can remove social context and relevance.Statistical Modeling, Causal Inference, and Social Science
Gaydar and the fallacy of objective measurement
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University