Statistical Analysis in Microbiology

Informationen zum Autor Dr. Richard Armstrong is a?Lecturer for the School of Life and Health Sciences for Aston University. His research interests include?Neuropathology of Alzheimer's Disease; Lichen Biology and Ecology. He has?published in many?peer reviewed journals. Dr. Anthony C. Hilton is a Senior Lecturer in Microbiology and the UG Programme Director at Aston University in the School of Life and Health Sciences. His research interests include applied microbiology (food and clinical), molecular microbiology, salmonella, campylobacter, MRSA and Escherichia?coli 0157, clostridium difficult, and molecular typing of microorganisms.?In addition to contributing to various peer reviewed journals, he is also member of the Molecular Biosciences Research Group. Klappentext 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. Zusammenfassung 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 scena...

Autorentext

Dr. Richard Armstrong is a?Lecturer for the School of Life and Health Sciences for Aston University. His research interests include?Neuropathology of Alzheimer's Disease; Lichen Biology and Ecology. He has?published in many?peer reviewed journals. Dr. Anthony C. Hilton is a Senior Lecturer in Microbiology and the UG Programme Director at Aston University in the School of Life and Health Sciences. His research interests include applied microbiology (food and clinical), molecular microbiology, salmonella, campylobacter, MRSA and Escherichia?coli 0157, clostridium difficult, and molecular typing of microorganisms.?In addition to contributing to various peer reviewed journals, he is also member of the Molecular Biosciences Research Group.

Klappentext

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.

Inhalt

Preface. Acknowledgments. Note on Statistical Software. 1 ARE THE DATA NORMALLY DISTRIBUTED? 1.1 Introduction. 1.2 Types of Data and Scores. 1.3 Scenario. 1.4 Data. 1.5 Analysis: Fitting the Normal Distribution. 1.6 Conclusion. 2 DESCRIBING THE NORMAL DISTRIBUTION. 2.1 Introduction. 2.2 Scenario. 2.3 Data. 2.4 Analysis: Describing the Normal Distribution. 2.5 Analysis: Is a Single Observation Typical of the Population? 2.6 Analysis: Describing the Variation of Sample Means. 2.7 Analysis: How to Fit Confidence Intervals to a Sample Mean. 2.8 Conclusion. 3 TESTING THE DIFFERENCE BETWEEN TWO GROUPS. 3.1 Introduction. 3.2 Scenario. 3.3 Data. 3.4 Analysis: The Unpaired t Test. 3.5 One-Tail and Two-Tail Tests. 3.6 Analysis: The Paired t Test. 3.7 Unpaired versus the Paired Design. 3.8 Conclusion. 4 WHAT IF THE DATA ARE NOT NORMALLY DISTRIBUTED? 4.1 Introduction. 4.2 How to Recognize a Normal Distribution. 4.3 Nonnormal Distributions. 4.4 Data Transformation. 4.5 Scenario. 4.6 Data. 4.7 Analysis: Mann-Whitney U test (for Unpaired Data). 4.8 Analysis: Wilcoxon Signed-Rank Test (for Paired Data). 4.9 Comparison of Parametric and Nonparametric Tests. 4.10 Conclusion. 5 CHI-SQUARE CONTINGENCY TABLES. 5.1 Introduction. 5.2 Scenario. 5.3 Data. 5.4 Analysis: 2 x 2 Contingency Table. 5.5 Analysis: Fisher's 2 x 2 Exact Test. 5.6 Analysis: Rows x Columns (R x C) Contingency Tables. 5.7 Conclusion. 6 ONE-WAY ANALYSIS OF VARIANCE (ANOVA). 6.1 Introduction. 6.2 Scenario. 6.3 Data. 6.4 Analysis. 6.5 Assumptions of AN…