The author reasons through statistical concepts as though he is sharing his uninterrupted thoughts. You may personally like or hate this style. I personally liked it because I found it easy to follow along with his linear thinking. The author simplifies statistical concepts for behavioral science students very well. He also makes use of very interesting examples (real studies) from the behavioral sciences to keep the reader engaged and mindful of how the learned methodologies apply to data, knowledge, and research in the field. Above all, I love how he includes data from real studies for us to perform our analyses in practice.
There are some shortcomings to the book. The book lacks in mathematical elaborations and explanations of formulas, but that is to be expected since this textbook is not aimed at a mathematical audience but at behavioral science students. Those students would likely not welcome additional mathematical derivations or explanations that are unnecessary for their practical training purposes. Moreover, although the book typically alludes to good quality studies, the author infrequently references studies with questionable methodologies, confounding variables, and hypotheses (e.g. pg. 404). Moreover, it is a little annoying how he frequently references studies discussed in prior chapters, causing students to have to skim back and locate the data to perform their calculations. Moreover, his writing can be at times ambiguous and explanations unnecessarily complicated. Overall, however, the author does a good job at making statistics understandable to a scientific audience without a mathematical background.