The quantisation of the world requires skilful application of increasing and changing information and these skills have become one important currency of knowledge. In order to answer to needs of the dynamic society trustworthy data on which to base choices and decisions are needed. In addition to the trustworthy data, also accurate and well- organised skills to analyse these data are required. Reliable statistics and inference based on these statistics belong to basic structure of a democratic state because they describe "the state of arts" in the society in a proper way. Therefore, accurate statistical methods can be seen as a cornerstone of democracy. The quantisation of the society has also led to a new way of thinking of the education of quantitative skills. Quantitative skills are not seen as an elite subject (eg. Moore 1997), they are seen more as a part of general education in modern society.One example of too simplified conclusions based on insufficient statistical analyses is the study of Richard Lynn and Tatu Vanhanen (2002). They have written a book on the relation of the average national intelligence (IQ) and the wealth of nations. They argue that the impact of average national IQ on the national per capita income (GDP) is so remarkable that the wealth of the nation, in practice, does not very much depend on any other factor. The statistical re-analyses of the same data by the authors (Rautopuro, Väisänen & Malin 2004) show, however, that controlling the effects of some other important factors, say, education, fertility and daily calorie intake fades away the association between the average national IQ and GPD. The conclusion based on this re-analysis is quite different from Lynn & Vanhanen: the differences between countries in their GDP may not be explained by the variation in average national IQ at all. The differences depend on completely different factors, say, equal possibilities for education for women. This result is somewhat parallel to the results of Volken (2003).The model of Lynn and Vanhanen is miss-specified since many potential important explanatory variables are not included in the model. This kind of misuse of statistical methods and the conclusions based on these results may have important policy consequences, not least to the decisions that deal with education. In this paper it is shown that proper discussion with data (accurate analyses), proper use of statistical methods and understanding the iterative nature of quantitative analysis (comparing different methods and models) must be required in academic research. As an example, diverse statistical models are fitted into the data used by Lynn and Vanhanen, and the results and conclusions are compared.References:Lynn, R. & Vanhanen, T. 2002. IQ and the Wealth of Nations. Wesport, Connecticut: Praeger.Lynn, R. & Vanhanen, T. 2002. Intelligence and the Wealth and Poverty of Nations. http://www.rlynn.co.uk/pages/articles.htm.Moore, D.S, 1997. New pedagogy and new content: the case of statistics. International Statistical Review 65 (2), 124-65.Rautopuro, J., Väisänen, P. & Malin, A. 2004. Sulje silmäsi vain… Tutkimustulosten päätelmät kuin iskelmien lempi. (Close Your Eyes… Research Results as Promises of Love Songs) Paper presented at the Annual Conference of the Finnish Educational Research Association, 25.11.2004, Joensuu, Finland.Vanhanen, T. 2004. Globaalisten kehityserojen juuret ihmisten erilaisuudessa. (The Roots of Differences in Global Development Lie in the Difference of People) Tieteessä Tapahtuu 6, 40-45. Volken, T. 2003. IQ and the Wealth of Nations. A Critique of Richard Lynn and Tatu Vanhanen's Recent Book. European Sociological Review, 19, 4, 411-412. Extended version: http://www.suz.unizh.ch/volken/pdfs/IQWealthNation.pdf