Multi-factor research design is widely applied in scientific research. It can simultaneously explore the effects of multiple factors on outcome indicators. The consideration of the interactive effects of different factors is a critical issue when analyzing this type of data. The analytic strategy for main effects or simple effects depends on the significance of the interactive effect. However, many researchers tend to skip the analysis on interactive effects, or wrongly select statistical analysis method because of ignoring the test result. In this study, SPSS 20.0 and R 3.6.1 statistical software were used to simulate and illustrate how to analyze data from two most popular multi-factor design data——factorial design and repeated measurement design. The significance of evaluating interactive effect and corresponding key point analysis was explained. The possible consequences of ignoring the statistical significance of interactive effects were indicated, that include leading to low inspection efficiency, prone to draw wrong conclusions, loss of valuable information in the original data, or loss of practical significance of the analytic results. It is suggested that in the analysis of research data, we should first judge whether there are interactive effects, and then correctly choose main effect analysis or single effect analysis to avoid one-sided and wrong conclusions.