Comparison between Common Statistical Modeling Techniques Used in Research, Including: Discriminant Analysis vs Logistic Regression, Ridge Regression vs LASSO, and Decision Tree vs Random Forest

Statistical techniques are important tools in modeling research work. However, there could be misleading outcomes if sufficient care is undermined in choosing the right approach. Employing the correct analysis in any research work needs deep knowledge on the differences between these tools. Incorrect selection of the modeling technique would create serious problems during the interpretation of the findings and could affect the conclusion of the study. Each technique has its own assumptions and procedures about the data. This paper compares common statistical approaches, including regression vs classification, discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest. Results show that each approach has its unique statistical characteristics that should be well understood before deciding upon its utilization in the research.