What Are Statistics: It's Time to set the Record Straight
https://doi.org/10.34023/2313-6383-2023-30-4-96-107
Abstract
The authors present their views on the modern concept of statistics as a science, academic discipline, and field of activity. The urgency of the matter lies in an adequate understanding of the essence of statistics, which determines the direction for its further evolution as a tool for increasing the effectiveness of the national socio-economic processes.
According to the authors, by the beginning of the XXI century, de jure statistics are associated with activities, measured by data, that reflect a set of phenomena of diverse nature while ignoring de facto activities, associated with an assessment of the quantitative side of mass phenomena as part of information support of cognitive processes and management decision-making in the socio-economic sphere. In the former case, theoretical, methodological, and practical statistical activities are based on the consideration of the statistical population as such, for which is being developed an appropriate toolkit based primarily on the unity of methods of mathematical logic, mathematical statistics, and big data analysis. In the latter case, theoretical, methodological, and practical activities are interpreted within the framework of the concept implying that the quantitative side of mass social phenomena is the object of cognition and management as an objective reality. It is based on the following categories: a statistical indicator, a system of indicators, and an information model.
Analysis of the main features of the two types of activity show that their convergence is impossible. The paper concludes that while maintaining the independent status of the first type of activity, called Statistics, it would be appropriate to acknowledge the independent status of the second type of activity, called Socio-economic statistics, as a type of occupation, a specialty and area of training in higher education, a scientific specialty – a branch of science, incorporating it in the professional standard «Statistician», the educational standard of higher education «Socio-economic statistics», and the standard of the scientific specialty «Socio-economic statistics».
About the Authors
O. E. MichnenkoRussian Federation
Dr. Sci. (Econ.), Professor; Professor, Department of Information Systems of the Digital Economy
9, Obraztsova Str., Bldg. 9, Moscow, 127994
V. N. Salin
Russian Federation
Cand. Sci. (Econ.), Professor, Department of Business Analytics
49/2, Leningradsky Ave., Moscow, 125167
References
1. Afanasyev V.G. Consistency and Society. Moscow: Politizdat Publ.; 1980. 368 p. (In Russ.)
2. Kedrov B.M. Categories of Marxist Dialectics as a Methodological Basis of Statistical Science. In: Academy of Sciences of the USSR. Scientific Notes on Statistics. Vol. VI. Moscow: Publ. House of the USSR Academy of Sciences; 1961. P. 5–38. (In Russ.)
3. Mikhnenko O.E. Information Models in the Management of Economic Phenomena. Moscow: MIIT; 2009. 48 p. (In Russ.)
4. Kendall M.G., Stuart A. The Advanced Theory of Statistics: in Three Volumes. Vol. 1. Distribution Theory. 2nd ed. London: Charles Griffin & Company Limited; 1963. Pp. xii + 433. (Russ. ed.: Kendall M.Dzh., St"yuart A. Teoriya raspredelenii. Moscow: Nauka Publ.; 1966. 587 p.).
5. Kendall M.G., Stuart A. The Advanced Theory of Statistics: in Three Volumes. Vol. 2. Inference and Relationship. London: Charles Griffin & Company Limited; 1961. Pp. ix + 676. (Russ. ed.: Kendall M.Dzh., St"yuart A. Statisticheskie vyvody i svyazi. Moscow: Nauka Publ.; 1973. 900 p.)
6. Kendall M.G., Stuart A. The Advanced Theory of Statistics: in Three Volumes. Vol. 3. Design and Analysis, and Time Series. London: Charles Griffin & Company Limited; 1966. Pp. ix + 552. (Russ. ed.: Kendall M.Dzh., St"yuart A. Mnogomernyi statisticheskii analiz i vremennye ryady. Moscow: Nauka Publ.; 1976. 736 p.)
7. Nivorozhkina L.I. Who Should Teach Statistics in the Digital Economy? In: Sadovnikova N.A. (ed.) Bulletin of the Department of Statistics of the Russian University of Economics named after G.V. Plekhanov. Statistical Studies of the Socio-Economic Development of Russia and Prospects for Sustainable Growth. Moscow: Plekhanov Russian University of Economics; 2018. P. 362–364. (In Russ.)
8. Mayer-Schönberger V., Cukier K. Big Data: A Revolution that Will Transform How We Live, Work, and Think. Boston, MA: Houghton Mifflin Harcourt; 2013. (Russ. ed.: Maier-Shenberger V., Kuk'er K. Bol'shie dannye. Revolyutsiya, kotoraya izmenit to, kak my zhivem, rabotaem i myslim. Moscow: Mann, Ivanov i Ferber Publ.; 2014. 240 p.)
9. Franks B. Taming the Big Data. Finding Opportunities in Huge Data Streams with Advanced Analytics. New York: John Wiley and Sons, Inc.; 2012. 336 p. (Russ. ed.: Frenks B. Ukroshchenie bol'shikh dannykh. Kak izvlekat' znaniya iz massivov informatsii s pomoshch'yu glubokoi analitiki. Moscow: Mann, Ivanov i Ferber Publ.; 2014. 343 p.)
10. Hassani H., Saporta G., Silva E.S. Data Mining and Official Statistics: The Past, the Present and the Future. Big Data. 2014;2(1):34–43. Available from: https://doi.org/10.1089/big.2013.0038.
11. Hostie T., Nibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2 nd Ed. New York: Springer-Verlag; 2009. 763 p.
12. Mikhnenko O.E., Salin V.N. From the Analysis of Statistical Data to the Analysis of Real Phenomena on the Basis of Statistical Information. In: Proc. of the Int. Sci.Pract. Conf. «Data Science», 5–7 February 2020, St. Petersburg. St. Petersburg: UNECON Publ.; 2020. P. 196–199. (In Russ.)
13. Maslov P.P. Statistics in Sociology. Moscow: Statistika Publ., 1971. 248 p. (In Russ.)
14. Mikhnenko O.E. Digital Technologies and the Effectiveness of Statistical Indicators. In: Sokolov Yu.I. et al. (eds) Digital Transformation in the Economy of the Transport Complex: Proc. of the Int. Sci. and Pract. Conf. Moscow: RUT (MIIT); 2019. P. 207–216. (In Russ.)
15. Mikhnenko O.E. On Measures of Labor Productivity. In: Statistical Studies of Russia's Socio-Economic Development and Prospects for Sustainable Growth: Materials and Reports. Moscow: Plekhanov Russian University of Economics; 2019. P. 108–114. (In Russ.)
16. Mikhnenko, O.E. Problems of Management of Economic Phenomena in Railway Transport: Information Aspect. Moscow: MIIT; 2001. 200 p.
17. Mikhnenko O.E., Salin V.N. Statistics in the Professional Standard «Statistician». In: Statistics – the Language of Digital Civilization: Proc. of the Int. Sci. and Pract. Conf. «II Open Russian Statistical Congress» (Rostov-on-Don, 2018, December, 4–6): in Two Volumes. Vol. 1. Russian Association of Statisticians, Federal State Statistics Service, Rostov State University of Economics (RSUE), Rostov Regional Branch of the Free Economic Society of Russia. Rostov-on-Don: Publ. Comp. «AzovPrint»; 2018. P. 670–678. (In Russ.)
18. Salin V.N., Mikhnenko O.E. Statistical Education in Economic Universities: Modern Quality and Prospects. In: Proc. of the Int. Sci.-Pract. Conf. «Data Science», 5–7 February 2020, St. Petersburg. St. Petersburg: UNECON Publ.; 2020. P. 268–269. (In Russ.)
Review
For citations:
Michnenko O.E., Salin V.N. What Are Statistics: It's Time to set the Record Straight. Voprosy statistiki. 2023;30(4):96-107. (In Russ.) https://doi.org/10.34023/2313-6383-2023-30-4-96-107