Stochastic process computational modeling for learning research
| Autor: | Oleksandr H. Kolgatin, Larisa S. Kolgatina, Nadiia S. Ponomareva |
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| Jazyk: | German<br />English<br />Ukrainian |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Освітній вимір, Vol 6 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2708-4604 2708-4612 |
| DOI: | 10.31812/educdim.4498 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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