Keven R. Murphy and Brett Myors. 1998. Statistical Power Analysis: A
Simple and General Model for Traditional and Modern Hypothesis Tests.
LEA Publishers. ISBN 0-8058-2947-4.
It's cheap, about 100 pages, well written, and doesn't skimp on topics.
It also pushes minimum effect null hypothesis which are arguably more
meaningful in the context of power analyses than zero point null
hypotheses. With traditional null hypotheses, regardless of the size of
effect greater than zero, you will be guaranteed enough power with
enough N, and when you can't reject, you can't really accept the null
as true either. If you don't know the difference between significance
and effect size in statistical testing, this is dangerous. With a
minimum effect size null hypothesis, in and experiment with enough
power, you can reject an effect you judge to be negligible and a
nonsignificant result means something (see Chapter 4). Note, unlike
zero point null hypotheses, it is harder to reject minimum effect size
null hypotheses with larger N.
My suggestion would be to get your friend to read this book.
