Statistical Analysis in Microbiology – StatNotes : StatNotes

Based on the highly successful STATNOTES articles published in Microbiologist by the Society for Applied Microbiology, this book presents the most useful statistical tests in a clear way by applying them to real experiments in microbiology, meeting the need for a book devoted to data analysis in the field.

This book is aimed primarily at microbiologists who are undertaking research, and who require a basic knowledge of statistics to analyse their experimental data. Computer software employing a wide range of data analysis methods is widely available to experimental scientists. The availability of this software, however, makes it even more essential that microbiologists understand the basic principles of statistics. Statistical analysis of data can be complex with many different methods of approach, each of which applies in a particular experimental circumstance. In addition, most statistical software commercially available is complex and difficult to use. Hence, it is easy to apply an incorrect statistical method to data and to draw the wrong conclusions from an experiment. The purpose of this book is an attempt to present the basic logic of statistics as clearly as possible and therefore, to dispel some of the myths that often surround the subject. The book is presented as a series of 2018Statnotes', many of which were originally published in the 2018Microbiologist' by the Society for Applied Microbiology, each of which deals with various topics including the nature of variables, comparing the means of two or more groups, non-parametric statistics, analysis of variance, correlating variables, and more complex methods such as multiple linear regression and factor analysis. In each case, the relevant statistical methods are illustrated with scenarios and real experimental data drawn from experiments in microbiology. The text will incorporate a glossary of the most commonly used statistical terms and a section to aid the investigator to select the most appropriate test.