<![CDATA[The importance of validity and reliability of data analysis results in scientific research is the main focus in this research. Violations of classical assumptions can be detrimental to interpretation and decision making, requiring adaptive approaches to overcome them. Model modification is a promising solution to improve the quality of data analysis in this context. This research uses a literature study method to understand the concept and implications of violating classical assumptions. The model modification approach is then implemented through non-parametric model selection, data transformation, and robust techniques. The use of this method is illustrated with case studies and complex data analysis. The analysis results show that the model modification provides the flexibility needed to overcome violations of classical assumptions. Data transformation, use of non-parametric models, and robust techniques have succeeded in increasing the accuracy of data analysis, especially in conditions of abnormality or heteroscedasticity. However, researchers need to carefully consider the risk of overfitting and the additional complexity that may arise. This research concludes that model modification can be an effective approach to overcome violations of classical assumptions in data analysis. The choice of model modification must be adjusted to the characteristics of the data and research objectives to minimize the risk of distorting the results.]]>
Copyrights © 2023
