A modern approach to statistics

John Wiley & Sons, 1999. £50.00 hbk, £18.99 pbk (viii + 249 pages)ISBN 0 471 98821 9 (hbk), 0 471 98822 7 (pbk)This is an introductory text about the applications of statistics for the analysis of data collected in biological experiments. The text is appropriate for the researcher who wishes to use the computer for data analysis rather than a calculational, formula-driven approach. To this end the author has streamlined this second edition by relying on the capabilities of a computer rather than on a calculator in performing the calculations required in analyzing data. He has selected the Minitab™ Statistical Package to implement procedures ranging from graphical presentations and descriptive statistics, one- and two-sample tests, one- and two-way analysis of variance, to simple linear regression and its applications in dose–response assays.Why has the author chosen Minitab over various other packages available? He cites its ability to handle large sets of data, and its wide range of procedures for recording, displaying and analyzing data. In addition, it offers high-speed computing, is compatible with Excel and other data analysis programs, and is said to be the most widely used statistics package.Alastair Wardlaw introduces each chapter with a notable quote, including one by Lord Rutherford in which he stated, ‘If your experiment needs statistics, you ought to have done a better experiment’. The author introduces inherent variation in data using a set of data consisting of pipette delivery weights. Fifteen students, each of who obtained three observations using each of three different pipettes, collected these data. These data are used to explain concepts throughout the first few chapters of the text. The author begins by explaining Minitab’s worksheet through simple data entry. In addition, he plainly demonstrates how to navigate through the large number of procedures available in Minitab by using a screen capture of dialog windows.Chapter titles use language that is descriptively user-oriented. One such example is Chapter 2: ‘How to Condense the Bulkiness of Data’ in which graphical and numerical descriptive methods are introduced. In this chapter, the author takes the opportunity to introduce the normal distribution by superimposing a normal curve on a mound-shaped histogram of a set of data, and then discusses various aspects of the normal distribution without giving its functional form. This is another attempt to make the text user-friendly without sacrificing required content. A distinction between quantitative measurements and counts or proportions is made early in the text owing to the differences in analytical treatment required.Chapters 3 and 4 address the analysis of measurement data through one- and two-way analysis of variance. Chapter 5, ‘Awkward Data Measurements,’ addresses the problems associated with violation of assumptions including unequal variances, skewness and non-normality. Wardlaw, unlike many authors, also discusses problems and practical solutions associated with indefinite responses whereby a measurement can be assigned to an undetected response that is less than an observable threshold. Nonparametric procedures and transformations are suggested as solutions for these problems. Chapters 6 and 7 address the analysis of count and proportion data. Count data are analyzed as having been generated from a Poisson distribution. In addition to large sample techniques for constructing confidence intervals for mean counts, Wardlaw also provides a table of appropriate confidence intervals based upon observed counts ranging from 1 to 20. Although not directly available in Minitab, the author provides a χ2-goodness-of-fit test for a Poisson distribution using Minitab’s calculator. Although the concepts of over- and under-dispersed distributions are introduced, no analysis for these data is presented. Proportion data is given a nonstandard treatment in which the Minitab binomial probability menu is used to generate confidence limits for an unknown proportion.The treatment of correlation and regression includes only simple linear regression accomplished using Minitab’s regression command. Multiple linear and possibly polynomial regressions could have been included here because their implementation in Minitab uses the same regression dialog box. However, what is of interest, is that in the next and last chapter, the author uses simple linear regression results to accomplish inverse estimation for a linear dose–response relationship (i.e. estimating the dose required to produce a given response as well as relative potency calculations for parallel line assays). These two procedures are not provided within the Minitab command menus, but are implemented using the calculator options. In addition, Minitab’s probit analysis command is demonstrated in the analysis of data from a quantal-response assay.It is obvious that this text was not written by a statistician for other statisticians, but rather by a scientist well versed in statistics for an audience of biologists. The inclusion of special topics such as the most probable number of microbial cells in a sample, inverse regression estimation, parallel line assays and probit analysis are most welcome in a text at this level. Alastair Wardlaw has produced a refreshing and useful book that can be used either in the classroom or as a reference text.