The article discusses how many researchers make mistakes when analyzing data using MANOVA by not fully understanding its multivariate aspects. The authors aim to correct this by providing a clear method to properly analyze multivariate effects in a dataset. They compare MANOVA with univariate analysis and provide a detailed example with real data to demonstrate their approach. Key findings include outlining the differences between the two types of analysis, offering methods to identify and test multivariate effects accurately, estimating effect sizes and confidence intervals, and presenting results in an APA style. The paper also addresses various issues related to their methods.