hank you for your clear and well-structured explanation of the differences among statistical techniques. I appreciate how you have explained all the differences between the various statistical methods in a clear and well-organised manner. This is well done describing how a research design (especially the volume that requires independent variables and their nature) can dictate the choice of the test to utilize (Yu et al., 2022). I really liked how you logically led me to t-tests, to ANOVA procedures, and finally to mixed factorial ANOVA, which is a good way of showing how analysis tools show development in answering more complex research issues. After exploring the use of mixed designs what I think you mentioned is worth going into more was this. I came to hear them referred to as a powerful one because they combine both within subject and between subject factors as you mentioned. They also have interpretative problems, however, when there exist interpersonal interactions. Questions Do you consider that the existence of substantive interactions can influence your understanding of main effects? Or, as an instance, an important interaction may indicate that the principal action of one variable is only ever true at particular levels of the other, which can take the results of a study radically new. As well, because your source (Yu et al., 2022) focuses on the impending transition to traditional tools of statistical analysis (t-tests and ANOVA) in favour of mixed-effects tools, I am asking: how will the future of statistical analysis change in your daily practice or area of practice? Mixed-effects models are becoming more popular as they are more flexible in terms of handling hierarchical or nested data structure, and missing data. Curiosorily would you say that you have encountered them or do you already view them having a place in your research? Thank you for providing and fully educational and solid foundation contributing to my development. Thank you for sharing your insights! Mario Reference Yu, Z., Guindani, M., Grieco, S. F., Chen, L., Holmes, T. C., & Xu, X. (2022). Beyond t-test and ANOVA: Applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron, 110(1), 21–35. https://doi.org/10.1016/j.neuron.2021.10.031 Kindell Fincher 6 hours ago, at 2DescriptionoftheDifferencesBetweenTechniques1.docx
