Most introductory statistics text-books are written
either in a highly mathematical style for an intended
readership of mathematics undergraduate students, or in
a recipe-book style for an intended audience of
non-mathematically inclined undergraduate or
postgraduate students, typically in a single discipline;
hence, ''statistics for biologists'', ''statistics for
psychologists'', and so on. An antidote to
technique-oriented service courses, this book is
different. It studiously avoids the recipe-book style
and keeps algebraic details of specific statistical
methods to the minimum extent necessary to understand
the underlying concepts. Instead, the text aims to give
the reader a clear understanding of how core statistical
ideas of experimental design, modelling and data
analysis are integral to the scientific method.Aimed
primarily at beginning postgraduate students across a
range of scientific disciplines (albeit with a bias
towards the biological, environmental and health
sciences), it therefore assumes some maturity of
understanding of scientific method, but does not require
any prior knowledge of statistics, or any mathematical
knowledge beyond basic algebra and a willingness to come
to terms with mathematical notation. Any statistical
analysis of a realistically sized data-set requires the
use of specially written computer software. An Appendix
introduces the reader to our open-source software of
choice, R, whilst the book's web-page includes
downloadable data and R code that enables the reader to
reproduce all of the analyses in the book and, with easy
modifications, to adapt the code to analyse their own
data if they wish. However, the book is not intended to
be a textbook on statistical computing, and all of the
material in the book can be understood without using
either R or any other computer software. |
|