If you buy just one book on statistics, this should be it. If mathematics scares you, get this book. If you are interested in how and why statistics works, get this book. If you want to improve the way you teach statistics, get this book.
For people first studying statistics, note that this book is written in ENGLISH! All formulas are written in English, not arcane mathematical symbols. For example, the formula for the arithmetic mean or average is:
The average of a list of numbers equals their sum, divided by how many there are.
That's it, no summation symbols and no variables with subscripts. The average is also described as:
Average of a list = sum of entries divided by the number of entries
The standard deviation (SD) is described:
SD = square root of (average of (deviations from the average)^2 )
(A deviation from the average is just the number minus the mean for the entire set of numbers. I've used "^2" to represent "squared" or "raising to the second power".)
The book is both easy and enjoyable to read. It is interesting reading and not just for statisticians. You get to read about important applications of statisitics in the real world (often including relevant historical details). There are also very well thought-out excercises that are realistic and yet can be easily computed by hand.
When I first found this book, I had finished by Ph. D. and had taught statistics for a number of years. Even though this is an introductory text book, I still learned a lot! It actually explains many important concepts that are often buried in the mathematics of other books. (For example, how many students understand the concept of "regression toward the mean"?) It completely changed the way I taught statistics. Especially when you are first starting to study statistics, you don't want the mathematics to obscure the statistical concepts. I've seen far too many students being able to do much of the mathematics but not having a clue about the statistical concept behind the method. They could do the computations but wouldn't know why they were doing them or when the method was appropriate to use.
The book consists of 29 chapters and covers design of experiments (comparative experiments), descriptive statistics (histograms, mean, standard deviation, normal distribution), correlation and regression, probability, chance variability (expected value and standard error), sampling (surveys, chance error), chance models (measurement error, genetics), and tests of significance (large sample tests for the mean and proportions, t-tests, and Chi-square tests).
