Statistical Analysis in Microbiology: StatNotes: StatNotes - Softcover

Statistical Analysis in Microbiology: StatNotes has beendesigned specifically for microbiologists who are involved inexperimental research and need to draw accurate conclusions fromtheir findings. It features twenty-eight StatNotes that togetherenable you to understand the basic principles of statistics, choosethe correct statistical methods to analyze your experimental data,and work with a variety of commercially available statisticalsoftware packages. These StatNotes are based on a series of populararticles published in Microbiologist, the official magazine of theSociety for Applied Microbiology. They explore a variety of topicsin statistics that are essential for analyzing experimental data,including:

• Nature of variables

• Comparison of means of two or more groups

• Non-parametric statistics

• Analysis of variance

• Correlation of variables

• Multiple linear regression and factor analysis

Throughout the book, the logic and mechanics of each statisticaltest presented are carefully explained. Moreover, each statisticaltest is illustrated with examples drawn from actual experiments andresearch data in microbiology. As a result, you can clearly see howeach test is interpreted and what types of conclusions can be drawnfrom the research data.

In the back of the book, you'll find a glossary of statisticalterms and their abbreviations as well as a summary of sample sizecalculations. You'll also find two appendices that help you selectthe most appropriate statistical tests based on the type ofresearch you're conducting and the type of data that you'regathering.

Given the wide variety of statistical software packages, it'sessential that you understand the underlying principles and purposeof each statistical method; applying an inappropriate statisticaltest can easily lead to the wrong conclusions. Written specificallyfor microbiologists, the highly acclaimed and popular StatNotesenable you to choose which statistical methods should be applied toanalyze and draw correct conclusions from your experimentaldata.