In the past, many studies were applied various statistical analysis methods to evaluate students' learning achievement and satisfaction for improving the effectiveness of online teaching. However, most of these decided to rely on relatively fixed fundamental quantitative methodologies to determine essential results. Few studies have adequately classified statistical methods in engineering education to critically consider correlational trends or causal mechanisms in the field and make research results more explanatory and inclusive. Therefore, our main challenge is appropriately selecting quantitative or qualitative statistical methods used in online engineering education to make the research results more convincing. To fill this 'gap,' this article re-examines previous papers to summarize a statistical method in the online engineering discipline from diverse perspectives and construct a new mechanism of evaluating statistical methods for effective research in this field. Our goal is to provide an unexplored review of statistical methods of the online teaching and learning process considering the engineering educational perspective.