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lhsi writes:
Some recent research has suggested that there is a link between certain foods such as chocolate and obtaining a Nobel prize. New research found dedicating a high proportion of GDP to research and a high number of scientific papers published were more accurate predictors.
From the article:
Several recent studies have described a strong correlation between nutritional or economic data and the number of Nobel awards obtained across a large range of countries. This sheds new light on the intriguing question of the key predictors of Nobel awards chances. However, all these studies have been focused on a single predictor and were only based on simple correlation and/or linear model analysis. The main aim of the present study was thus to clarify this debate by simultaneously exploring the influence of food consumption (cacao, milk, and wine), economic variables (gross domestic product) and scientific activity (number of publications and research expenditure) on Nobel awards. An innovative statistical analysis, hierarchical partitioning, has been used because it enables us to reduce collinearity problems by determining and comparing the independent contribution of each factor. Our results clearly indicate that a country's number of Nobel awards can be mainly predicted by its scientific achievements such as number of publications and research expenditure. Conversely, dietary habits and the global economy variable are only minor predictors; this finding contradicts the conclusions of previous studies. Dedicating a large proportion of the GDP to research and to the publication of a high number of scientific papers would thus create fertile ground for obtaining Nobel awards.
(Score: 0) by Anonymous Coward on Saturday March 29 2014, @04:47PM
It's easy to try to sound smart and say things like that, but there are lots of things out there that can't be researched using controlled experiments. Thus, correlative post-hoc analyses are sometimes all that is available. So long as one is aware of the drawbacks of such studies, there is no reason not to do them. The problem is that it's too easy to go beyond the data and such studies are too often over-hyped and misunderstood by the media. Thus, a perfectly valid research approach gets a bad name.