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Summary

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.

Table of contents

Preface.
Acknowledgments.
Note on Statistical Software.
1 ARE THE DATA NORMALLY DISTRIBUTED?
2 DESCRIBING THE NORMAL DISTRIBUTION.
3 TESTING THE DIFFERENCE BETWEEN TWO GROUPS.
4 WHAT IF THE DATA ARE NOT NORMALLY DISTRIBUTED?
5 CHI-SQUARE CONTINGENCY TABLES.
6 ONE-WAY ANALYSIS OF VARIANCE (ANOVA).
7 POST HOC TESTS.
8 IS ONE SET OF DATA MORE VARIABLE THAN ANOTHER?
9 STATISTICAL POWER AND SAMPLE SIZE.
10 ONE-WAY ANALYSIS OF VARIANCE (RANDOM EFFECTS MODEL): THE NESTED OR HIERARCHICAL DESIGN.
11 TWO-WAY ANALYSIS OF VARIANCE.
12 TWO-FACTOR ANALYSIS OF VARIANCE.
13 SPLIT-PLOT ANALYSIS OF VARIANCE.
14 REPEATED-MEASURES ANALYSIS OF VARIANCE.
15 CORRELATION OF TWO VARIABLES.
16 LIMITS OF AGREEMENT.
17 NONPARAMETRIC CORRELATION COEFFICIENTS.
18 FITTING A REGRESSION LINE TO DATA.
19 USING A REGRESSION LINE FOR PREDICTION AND CALIBRATION.
20 COMPARISON OF REGRESSION LINES.
21 NONLINEAR REGRESSION: FITTING AN EXPONENTIAL CURVE.
22 NONLINEAR REGRESSION: FITTING A GENERAL POLYNOMIAL-TYPE CURVE.
23 NONLINEAR REGRESSION: FITTING A LOGISTIC GROWTH CURVE.
24 NONPARAMETRIC ANALYSIS OF VARIANCE.
25 MULTIPLE LINEAR REGRESSION.
26 STEPWISE MULTIPLE REGRESSION.
27 CLASSIFICATION AND DENDROGRAMS.
28 FACTOR ANALYSIS AND PRINCIPAL COMPONENTS ANALYSIS.

Authors

Armstrong, R. - Hilton, A.