Stochastic process computational modeling for learning research




computational modeling, computer-based simulation, statistical hypothesis significance testing, education, learning research


The goal of our research was to compare and systematize several approaches to non-parametric null hypothesis significance testing using computer-based statistical modeling. For teaching purposes, a statistical model for simulation of null hypothesis significance testing was created. The results were analyzed using Fisher's angular transformation, Chi-square, Mann-Whitney, and Fisher's exact tests. Appropriate software was created, allowing us to recommend new illustrative materials for expressing the limitations of the tests that were examined. Learning investigations as a technique of comprehending inductive statistics has been proposed, based on the fact that modern personal computers can run simulations in a reasonable amount of time with great precision. The collected results revealed that the most often used non-parametric tests for small samples have low power. Traditional null hypothesis significance testing does not allow students to analyze test power because the true differences between samples are unknown. As a result, in Ukrainian statistical education, including PhD studies, the emphasis must shift away from null hypothesis significance testing and toward statistical modeling as a modern and practical approach of establishing scientific hypotheses. This finding is supported by scientific papers and the American Statistical Association's recommendation.


Download data is not yet available.


Metrics Loading ...


Berkson, J.: In dispraise of the exact test: Do the marginal totals of the 2x2 table contain relevant information respecting the table proportions? Journal of Statistical Planning and Inference 2(1), 27–42 (1978). DOI:

Billiet, P.: The Mann-Whitney U-test – analysis of 2-between-group data with a quantitative response variable (2003),

Bilousova, L.I., Kolgatin, O.H., Kolgatina, L.S., Kuzminska, O.H.: Introspection as a condition of students’ self-management in programming training. In: Proceedings of the 1st Symposium on Advances in Educational Technology - Volume 1: AET. pp. 142–153. INSTICC, SciTePress (2022). Dimension (2022) DOI:

Bilousova, L.I., Kolgatina, L.S., Kolgatin, O.H.: Computer simulation as a method of learning research in computational mathematics. CEUR Workshop Proceedings 2393, 880–894 (2019)

Bradley, D.R., Hemstreet, R.L., Ziegenhagen, S.T.: A simulation laboratory for statistics. Behavior Research Methods, Instruments, and Computers 24(2), 190–204 (1992)., DOI:

Castro Sotos, A.E., Vanhoof, S., Van den Noortgate, W., Onghena, P.: How confident are students in their misconceptions about hypothesis tests? Journal of Statistics Education 17(2) (2009). DOI:

D’Agostino, R.B., Chase, W., Belanger, A.: The appropriateness of some common procedures for testing the equality of two independent binomial populations. The American Statistician 42(3), 198–202 (1988), DOI:

Fay, M.P., Proschan, M.A.: Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistics Surveys 4, 1–39 (2010). DOI:

Flusser, P., Hanna, D.: Computer simulation of the testing of a statistical hypothesis. Mathematics and Computer Education 25(2), 158 (1991),

Fong, Y., Huang, Y.: Modified Wilcoxon-Mann-Whitney test and power against strong null. The American Statistician 73(1), 43–49 (2019). DOI:

Gubler, Y.V., Genkin, A.A.: Primeneniye Neparametricheskikh Metodov Statistiki v Mediko-Biologicheskikh Issledovaniyakh (Application of Nonparametric Methods of Statistics in Biomedical Research). Meditsina, Leningradskoye otdeleniye, Leningrad (1973)

Jamie, D.M.: Using computer simulation methods to teach statistics: A review of the literature. Journal of Statistics Education 10(1) (2002). DOI:

Kanji, G.K.: 100 Statistical Tests. SAGE Publications, London - Thousand Oaks - New Delhi (2006)

Khazina, S.A., Ramskyi, Y.S., Eylon, B.S.: Computer modeling as a scientific means of training prospective physics teachers. In: EDULEARN16 Proceedings. pp. 7699–7709. 8th International Conference on Education and New Learning Technologies, IATED (4-6 July 2016). DOI:

Kolgatin, O.: Computer-based simulation of stochastic process for investigation of efficiency of statistical hypothesis testing in pedagogical research. Journal of Information Technologies in Education (ITE) (27), 007–014 (Oct 2016)., DOI:

Kolgatin, O.H.: Informatsionnyye tekhnologii v nauchno-pedagogicheskikh issledovaniyakh (Information technologies in educational researches). Upravlyayushchiye Sistemy i Mashiny (Control Systems and Machines) 255(1), 66–72 (2015)

Kolgatin, O.H., Kolgatina, L.S., Ponomareva, N.S., Shmeltser, E.O., Uchitel, A.D.: Systematicity of students’ independent work in cloud learning environment of the course "educational electronic resources for primary school" for the future teachers of primary schools. In: Proceedings of the 1st Symposium on Advances in Educational Technology - Volume 1: AET. pp. 538–549. INSTICC, SciTePress (2022). DOI:

Kravtsov, H.M.: Methods and technologies for the quality monitoring of electronic educational resources. CEUR Workshop Proceedings 1356, 311–325 (2015)

Lang, K.M., Sweet, S.J., Grandfield, E.M.: Getting beyond the Null: Statistical Modeling as an Alternative Framework for Inference in Developmental Science. Research in Human Development 14(4), 287–304 (2017). DOI:

Liddell, D.: Practical tests of 2 × 2 contingency tables. Journal of the Royal Statistical Society. Series D (The Statistician) 25(4), 295–304 (1976). DOI:

Markova, O., Semerikov, S., Popel, M.: CoCalc as a learning tool for neural network simulation in the special course “Foundations of mathematic informatics”. CEUR Workshop Proceedings 2104, 388–403 (2018) DOI:

Marx, A., Backes, C., Meese, E., Lenhof, H.P., Keller, A.: EDISON-WMW: Exact dynamic programing solution of the Wilcoxon-Mann-Whitney test. Genomics, Proteomics and Bioinformatics 14(1), 55–61 (2016). DOI:

McShane, B.B., Gal, D., Gelman, A., Robert, C., Tackett, J.L.: Abandon Statistical Significance. The American Statistician 73(sup1), 235–245 (2019). DOI:

Modlo, Y.O., Semerikov, S.O.: Xcos on Web as a promising learning tool for Bachelor’s of Electromechanics modeling of technical objects. CEUR Workshop Proceedings 2168, 34–41 (2018)

Preacher, K.J.: Calculation for Fisher’s exact test (2021),

Ricketts, C., Berry, J.: Teaching statistics through resampling. Teaching Statistics 16(2), 41–44 (1994). DOI:

Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Y.V., Kiv, A.E.: Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. CEUR Workshop Proceedings 2257, 122–147 (2018) DOI:

Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Y.V., Markova, O.M., Soloviev, V.N.: Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. CEUR Workshop Proceedings 2393, 833–848 (2019) DOI:

Sidorenko, Y.V.: Metody Matematicheskoy Obrabotki v Psikhologii (Methods of Mathematical Processing in Psychology). Rech, St. Petersburg (2002),

Taylor, D.W., Bosch, E.G.: CTS: A clinical trials simulator. Statistics in Medicine 9(7), 787–801 (1990). DOI:

Verma, J.P.: Data Analysis in Management with SPSS Software. Springer, India (2013). DOI:

Wasserstein, R.L., Lazar, N.A.: The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician 70(2), 129–133 (2016). DOI:

Wasserstein, R.L., Schirm, A.L., Lazar, N.A.: Moving to a World Beyond “p < 0.05”. The American Statistician 73(sup1), 1–19 (2019). DOI:




How to Cite

Kolgatin, O., Kolgatina, L., & Ponomareva, N. (2022). Stochastic process computational modeling for learning research. Educational Dimension, 58, 68–83.



Theories of Learning, Education and Training