Biostatistics: A Foundation for Analysis in the Health Sciences
(8e) by Wayne Daniel is a textbook appropriate for advanced undergraduate and beginning graduate students, as well as a useful reference for researchers in the health and biological fields. While a number of texts exist for these audiences, this has been written at a more accessible level for those without any mathematical exposure beyond algebra. This accessibility should be particularly advantageous for those looking to use it as a text for advanced undergraduates.
The book begins laying the groundwork for statistical inference as well as providing some background in sampling in the first chapter. While oftentimes this is not done until after probability has been presented, Daniel uses it as motivation for the development of the theory that is needed for formal inference procedures later in the text. This also provides the student with no statistical background some insight into the notational differences seen in the following chapter on descriptive statistics.
All the standard topics that are generally taught in an introductory statistics course are covered. Specialized topics such as diagnostic testing, logistic regression, survival analysis, and life tables make it especially appealing for those in a biostatistics course, or for those wishing to use it as a basic reference in a medical or biological research setting. In addition, the text has impressive coverage of nonparametric and distribution-free statistical methods for hypothesis testing, analysis of variance, and regression analysis.
The text makes use of statistical software packages throughout to demonstrate many of statistical procedures it presents. MINITAB is the primary package used and many of the necessary commands are given for readers who wish to take advantage of software to assist them in analyzing data. In addition to MINITAB, SAS output is given for a few topics such as contingency table analysis and analysis of variance. For survival analysis, SPSS is the primary package used with details given on how to implement the methods covered in the text.
While some texts that incorporate statistical software commands and output often are difficult to use for those who prefer an alternate package (or no package at all), this is not true of Danielâ€™s book. Readers not using the same packages should not find the information on software distracting. This adds to its broad appeal and reinforces its appropriateness for a variety of audiences seeking a comprehensive summary of basic methods used to analyze data â€“ particularly in the biological and health sciences.