Second, the manager must understand what central tendency and variability mean. Specifically, these are simply measures showing where the data trends lie, and how spread out the data are.

Third, other descriptive statistics such as association and dispersion should be understood so that the basic relationships within data are understood. If customers who buy one product are more (or less) likely to also buy another product, the manager needs to know that in developing marketing around the two products.

Fourth, the manager should have a basic understanding of correlation and regression, including an understanding of how valid and reliable measures are formulated and reported. This requires the manager to know something about basic applications of methodology and something about strength and direction factors in such commonly used techniques as Chi-square, ANOVA, and t-tests. At a minimum, the manager must know that correlation implies association between two variables, while regression includes a predictive element. Understanding typical statistical measures, error types and confidence/significance levels for different sample sizes will allow the manager to effectively use relevant numbers.

Fifth, the manager must know how to assess theoretical and normal distributions so that decisions can be made based on how far the data depart from what might be expected given the parameters of the data.

Sixth, and finally, basic sampling techniques, hypothesis formulation and testing, and research design methdologies should be understood...

Nelson and Crotty (n.d.) describe these as, among other values, a commitment to objectivity and neutrality in dealing with number-driven data (4-5). While a decision can be made in any direction the manager pleases once the data is compiled and analyzed, the treatment of the numbers themselves should be conducted with a view to see what is actually there and how it is reflected in the statistical underpinnings of the analysis. In order to be able to handle these responsibilities appropriately, the manager must first have a firm grounding in, at a minimum, a basic level of statistical competence.
Works Cited

Nelson, L., and Crotty, M. (n.d.). The Ethical Use of Statistics in Research. North Carolina State University. December 10, 2009. Retrieved from http://faculty.chass.ncsu.edu/comstock/langure/modules/documents/Ethicaluseofstatisticsdraft1.doc.

Spatz, C. (2008). Basic Statistics: Tales of Distributions, 9th Edition. Cincinnati: Thomson/Wadsworth.

Urdan, T. (2005). Statistics in plain English. Mahwah, NJ: Lawrence Erlbaum Associates.

Wegner, T. (2008). Applied business statistics: methods and excel-based applications. Capetown, SA: Juta & Co.