1 Affiliation : Docente de la carrera de Derecho de la Universidad Regional Autónoma de los Andes (UNIANDES Babahoyo), Ecuador
Email : firstname.lastname@example.org
2 Affiliation : Docente de la carrera de Derecho de la Universidad Regional Autónoma de los Andes (UNIANDES Riobamba), Ecuador
Email : email@example.com
3 Affiliation : Docente de la carrera de Derecho de la Universidad Regional Autónoma de los Andes (UNIANDES Tulcán), Ecuador
Email : firstname.lastname@example.org
Social Science deals with the study of phenomena related to the social status of human beings. The importance of such sciences lies in the fact that they make it possible to know, predict, modify and improve the functioning of human societies today. Due to the great complexity of modern societies, it is virtually impossible to have accurate data or knowledge about any contemporary society. That is why neutrosophic theory is suitable for representing and modeling the data from studies on any social sciences. They may contain data that is contradictory, incomplete, inaccurate, vague, and so on. In particular, neutrosophic statistics generalizes classical statistics to interval-valued data. Since classical statistics are of great importance for the study of Social Sciences. We will emphasize the Legal Sciences in our approach
Neutrosophic; Social Science; Statistical; Neutrosophy;
 F. Smarandache, A unifying field in logics: neutrosophic logic. Neutrosophy, neutrosophic set, neutrosophic probability: neutrosophic logic. Neutrosophy, neutrosophic set, neutrosophic probability. Infinite Study, 2005.
 F. Smarandache, “A unifying field in Logics: Neutrosophic Logic.,” in Philosophy, American Research Press, 1999, pp. 1–141.
 S. M. Lynch, Introduction to applied Bayesian statistics and estimation for social scientists, vol. 1. Springer, 2007.
 A. Desrosières, “How to make things which hold together: Social science, statistics and the state,” in Discourses on society, Springer, 1990, pp. 195–218.
 C. Chatfield, Statistics for technology: a course in applied statistics. Routledge, 2018.
 Y. M. Zhukov, “Applied spatial statistics in r,” IQSS, Harvard University, 2010.
 R. J. A. Little and D. B. Rubin, “The analysis of social science data with missing values,” Sociological methods & research, vol. 18, no. 2–3, pp. 292–326, 1989.
 A. P. Rovai, J. D. Baker, and M. K. Ponton, Social science research design and statistics: A practitioner’s guide to research methods and IBM SPSS. Watertree Press LLC, 2013.
 M. A. R. Townsend, D. W. Moore, B. F. Tuck, and K. M. Wilton, “Self‐concept and anxiety in university students studying social science statistics within a co‐operative learning structure,” Educational Psychology, vol. 18, no. 1, pp. 41–54, 1998.
 S. Jackman, “Estimation and inference are missing data problems: Unifying social science statistics via Bayesian simulation,” Political Analysis, vol. 8, no. 4, pp. 307–332, 2000.
 D. Hicks, "The four kinds of literature of social science," Handbook of quantitative science and technology research, pp. 473–496, 2004.
 Mikail Bal , Katy D. Ahmad , Rozina Ali, A Review On Recent Developments In Neutrosophic Linear Diophantine Equations, Journal of Neutrosophic and Fuzzy Systems, Vol. 2 , No. 1 , (2022) : 61-75
 I. Deutscher, “Words and deeds: Social science and social policy,” Social problems, vol. 13, no. 3, pp. 235–254, 1966.
 L. M. Friedman, The legal system: A social science perspective. Russell Sage Foundation, 1975.
 B. Wootton and S. Miller, “Social science & social pathology,” Social Work (1939-1970), vol. 16, no. 4, pp. 122–126, 1959.
 P. Winch, The idea of social science and its relation to philosophy. Routledge, 2015.
 P. T. Manicas, “History and philosophy of social science,” 1991.
 D. R. Krathwohl, Methods of educational and social science research: An integrated approach. Longman/Addison Wesley Longman, 1993.
 R. L. Meek, Social science and the ignoble savage. Cambridge University Press, 2011.
 M. Rein, Social science and public policy. Penguin Books New York, 1976.
 J. Gerring, Social science methodology: A criterial framework. Cambridge university Press, 2001.
 D. C. Phillips, Philosophy, science, and social inquiry: Contemporary methodological controversies in social science and related applied fields of research. Pergamon Press, 1987.
 J. Monahan and L. Walker, "Social Authority: Obtaining, evaluating, and establishing social science in law," U. Pa. L. Rev., vol. 134, p. 477, 1985.
 G. Thomas, “A typology for the case study in social science following a review of definition, discourse, and structure,” Qualitative inquiry, vol. 17, no. 6, pp. 511–521, 2011.
 D. Ross, The origins of American social science, no. 19. Cambridge University Press, 1992.
 C. E. Lindblom and D. K. Cohen, Usable knowledge: Social science and social problem solving, vol. 21. Yale University Press, 1979.
 M. Hollis, The philosophy of social science: An introduction. Cambridge University Press, 1994.
 J. M. Epstein, “Generative social science,” in Generative Social Science, Princeton University Press, 2012.
 J. C. S. Morán, J. F. E. Chuga, and W. M. Arias, “Neutrosophic statistics applied to the analysis of socially responsible participation in the community,” Neutrosophic Sets and Systems, Book Series, vol. 26, p. 18, 2019.
 M. Aslam, R. A. R. Bantan, and N. Khan, “Design of a new attribute control chart under neutrosophic statistics,” International Journal of Fuzzy Systems, vol. 21, no. 2, pp. 433–440, 2019.
 M. Aslam, O. H. Arif, and R. A. K. Sherwani, “New diagnosis test under the neutrosophic statistics: an application to diabetic patients,” BioMed Research International, vol. 2020, 2020.
 F. Smarandache, Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset. Similarly for Neutrosophic Over-/Under-/Off-Logic, Probability, and Statistics. Infinite Study, 2016.
 J. Chen, J. Ye, and S. Du, “Scale effect and anisotropy analyzed for neutrosophic numbers of rock joint roughness coefficient based on neutrosophic statistics,” Symmetry, vol. 9, no. 10, p. 208, 2017.
 M. Aslam and O. H. Arif, "Testing of grouped product for the Weibull distribution using neutrosophic statistics," Symmetry, vol. 10, no. 9, p. 403, 2018.
 L. E. V. Cruzaty, M. R. Tomalá, and C. M. C. Gallo, “A Neutrosophic Statistic Method to Predict Tax Time Series in Ecuador,” Neutrosophic Sets and Systems, vol. 34, pp. 33–39, 2020.
 P. A. M. Silva, A. R. Fernández, and L. A. G. Macías, Neutrosophic Statistics to Analyze Prevalence of Dental Fluorosis, vol. 37. Infinite Study, 2020.
 M. Aslam, “Neutrosophic analysis of variance: application to university students,” Complex & intelligent systems, vol. 5, no. 4, pp. 403–407, 2019.
 M. Aslam and M. Albassam, “Presenting post hoc multiple comparison tests under neutrosophic statistics,” Journal of King Saud University-Science, vol. 32, no. 6, pp. 2728–2732, 2020.
 Prem Kumar Singh , Katy D. Ahmad , Mikail Bal , Malath Aswad, On The Symbolic Turiyam Rings, Journal of Neutrosophic and Fuzzy Systems, Vol. 1 , No. 2 , (2021) : 80-88
 M. Aslam, “Analyzing wind power data using analysis of means under neutrosophic statistics,” Soft Computing, vol. 25, no. 10, pp. 7087–7093, 2021.
 M. Aslam, “A new sampling plan using neutrosophic process loss consideration,” Symmetry, vol. 10, no. 5, p. 132, 2018.
 Mikail Bal , Katy D. Ahmad , Arwa A. Hajjari , Rozina Ali, A Short Note on the Kernel Subgroup of Intuitionistic Fuzzy Groups, Journal of Neutrosophic and Fuzzy Systems, Vol. 2 , No. 1 , (2022) : 14-20
 M. Aslam, N. Khan, and M. Z. Khan, “Monitoring the variability in the process using neutrosophic statistical interval method,” Symmetry, vol. 10, no. 11, p. 562, 2018.