Preface
This seventh edition of Statistical Methods for Psychology, like the previous editions, sur-
veys statistical techniques commonly used in the behavioral and social sciences, especially
psychology and education. Although it is designed for advanced undergraduates and grad-
uate students, it does not assume that students have had either a previous course in statis-
tics or a course in mathematics beyond high-school algebra. Those students who have had
an introductory course will find that the early material provides a welcome review. The
book is suitable for either a one-term or a full-year course, and I have used it successfully
for both. Since I have found that students, and faculty, frequently refer back to the book
from which they originally learned statistics when they have a statistical problem, I have
included material that will make the book a useful reference for future use. The instructor
who wishes to omit this material will have no difficulty doing so. I have cut back on that
material, however, to include only what is still likely to be useful. The idea of including
every interesting idea had led to a book that was beginning to be daunting.
My intention in writing this book was to explain the material at an intuitive level. This
should not be taken to mean that the material is “watered down,” but only that the empha-
sis is on conceptual understanding. The student who can successfully derive the sampling
distribution of t, for example, may not have any understanding of how that distribution is
to be used. With respect to this example, my aim has been to concentrate on the meaning
of a sampling distribution, and to show the role it plays in the general theory of hypothesis
testing. In my opinion, this approach allows students to gain a better understanding, than
would a more technical approach, of the way a particular test works and of the interrela-
tionships among tests.
Contrary to popular opinion, statistical methods are constantly evolving. This is in part
because psychology is branching into many new areas and in part because we are finding
better ways of asking questions of our data. No book can possibly undertake to cover all of
the material that needs to be covered, but it is critical to prepare students and professionals
to be able to take on that material when it is needed. For example, multilevel / hierarchical
models are becoming much more common in the research literature. An understanding of
these models requires specialized texts, but an understanding of fixed versus random
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