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.


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How to Cite

Kolgatin, O.H., Kolgatina, L.S. and Ponomareva, N.S., 2022. Stochastic process computational modeling for learning research. Educational Dimension [Online], 6, pp.68–83. Available from: [Accessed 14 June 2024].
Received 2021-06-28
Accepted 2022-02-06
Published 2022-06-14

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