I am hoping you can give me some suggestions. I am teaching in a very diverse (made of minority groups) college and the students are mostly Psychology majors. Most students are fresh from high school but some of them are older returning students above 40. Most of the students have motivational problems and aversion to math. But I am still looking for a book that covers the basic curriculum: from descriptive to sampling and testing all the way to ANOVA, and all in the context of experimental methods. The department requires me to use SPSS in class, but I like the idea of building the analysis in a spreadsheet such as excel.
p.s. the other teachers use a book that I don't like because of the extensive reliance on computational formulae. I find using these computational formulas – rather than the more intuitive and computationally intensive formula that is consistent with the rational and basic algorithm- unintuitive, unnecessary and confusing. This is the book I refer to Essentials of Statistics for the Behavioral Sciences, 7th Edition Frederick J Gravetter State University of New York, Brockport Larry B. Wallnau State University of New York, Brockport ISBN-10: 049581220X
Thank you for reading!
Best Answer
Statistics, by Freedman, Pisani, & Purves, originated from a popular and successful course taught at U.C. Berkeley. I have used it as an intro stats text for undergraduates, have borrowed some of its ideas when teaching graduate stats courses, and have given away many copies to colleagues and clients. There are many reasons for its popularity:
Its narrative and its problems are driven by real case studies and actual data of obvious importance, rather than the made-up drivel found in so many texts. These are truly interesting and memorable, including the Salk polio vaccine trials, the 1936 Literary Digest poll debacle, the Berkeley graduate student discrimination lawsuit (hinging on Simpson's Paradox), Fisher's criticism of Mendel's pea results, and much more.
It has extensive problems at three levels: at the end of each chapter subsection (of which there are hundreds), at the end of each chapter (over 30), and at the ends of major groups of chapters (about 4, I recall). These problems require minimal or no mathematics: they focus on potential misunderstandings that the authors, in their extensive experience, have found to arise among students.
It focuses on statistical ideas and reasoning rather than mathematics.
It uses (almost) no mathematical formulas. Quantitative relationships are usually expressed graphically and in words. (They are so clearly conveyed that when I first read this book, as a math graduate student entirely ignorant of statistics, I was able to reproduce all the underlying mathematical theory with no trouble.)
It covers most of the traditional material, including the Binomial and Normal distributions, confidence intervals, z tests, t tests, chi squared tests, regression, and the minimum amount of probability and combinatorics needed to understand these.
Some potential drawbacks would include:
No treatment of Bayesian statistics. This will make this book outmoded within a decade.
No treatment of ANOVA (psychology students might miss this the most).
No discussion of computing.
I believe the latter two are not critical: a good instructor can easily supply the ANOVA material and can teach as much or little computing as they might wish. Whether the omission of Bayesian statistics is important will depend on the instructor's tastes and aims.
Finally, I should note that although the mathematical demands are as small as one could possibly imagine, my pre- and post-testing of students indicates that people who come to the book with a disposition and habit of thinking quantitatively still get much more out of it than those who do not. Most of my students performed badly on pretests of mathematical knowledge (90% got failing grades), but those who also performed badly on pretests of critical thinking (Shane Frederick's Cognitive Reflection Test) exhibited markedly less improvement during the semester than others did. The pre and post tests both included the full 40-item CAOS test of fundamental concepts any introductory college-level stats course ought to include. The students in this class have consistently exhibited twice as much improvement as that reported in the CAOS literature; the students with poor cognitive reflection scores improved only an average amount (or failed to complete the course). I haven't the data to assign causes to this extra improvement, but suspect the textbook deserves at least some of the credit.