title: Let the numbers speak.
abstract: Although it is often claimed that statistical techniques are ways of letting the objective data speak for themselves, in both the contrast and correlational modes of statistical inference, all the real work is done by the a priori decisions imported into the analysis--which categories are to be used to create contrasting populations, which categories are to be measured, which categories are to be held constant while others are compared, and which is cause and which is effect? The authors explore here the problem of directionality of causation and the relationship between cause and effect, on the one hand, and dependent and independent variables, on the other. In systems of any complexity there are feedbacks--negative and positive feedbacks forming loops, embedded in larger contexts and subject to influences that can impinge on the loop at any point, such that the same pair of variables may show positive correlations in some situations and negative correlations in others.
authors: Department of Population and International Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA.
authors: R
authors: R
authors: Lewontin
authors: R
authors: R
authors: Levins
mesh_terms: D015984:Causality; D006304:Health Status; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D013597:Systems Analysis