Statistical analysis in microbiology Statnotes 1st Edition by Richard A Armstrong, Anthony C Hilton ISBN 0470905174 9780470905173

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ISBN 10: 0470905174 
ISBN 13: 9780470905173
Author:  Richard A Armstrong, Anthony C Hilton

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.

Statistical analysis in microbiology Statnotes 1st Table of contents:

• 1: ARE THE DATA NORMALLY DISTRIBUTED?
  • Introduction
  • Types of Data and Scores
  • Scenario
  • Data
  • Analysis: Fitting the Normal Distribution
    • How Is the Analysis Carried Out?
    • Interpretation
  • Conclusion
• 2: DESCRIBING THE NORMAL DISTRIBUTION
  • Introduction
  • Scenario
  • Data
  • Analysis: Describing the Normal Distribution
    • Mean and Standard Deviation
    • Coefficient of Variation
    • Equation of the Normal Distribution
  • Analysis: Is a Single Observation Typical of the Population?
    • How Is the Analysis Carried Out?
    • Interpretation
  • Analysis: Describing the Variation of Sample Means
  • Analysis: How to Fit Confidence Intervals to a Sample Mean
  • Conclusion
• 3: TESTING THE DIFFERENCE BETWEEN TWO GROUPS
  • Introduction
  • Scenario
  • Data
  • Analysis: The Unpaired t Test
    • How Is the Analysis Carried Out?
    • Interpretation
  • One-Tail and Two-Tail Tests
  • Analysis: The Paired t Test
  • Unpaired versus the Paired Design
  • Conclusion
• 4: WHAT IF THE DATA ARE NOT NORMALLY DISTRIBUTED?
  • Introduction
  • How to Recognize a Normal Distribution
  • Nonnormal Distributions
  • Data Transformation
  • Scenario
  • Data
  • Analysis: Mann–Whitney U test (for Unpaired Data)
    • How Is the Analysis Carried Out?
    • Interpretation
  • Analysis: Wilcoxon Signed-Rank Test (for Paired Data)
    • How Is the Analysis Carried Out?
    • Interpretation
  • Comparison of Parametric and Nonparametric Tests
  • Conclusion
• 5: CHI-SQUARE CONTINGENCY TABLES
  • Introduction
  • Scenario
  • Data
  • Analysis: 2 × 2 Contingency Table
    • How Is the Analysis Carried Out?
    • Interpretation
    • Yates’ Correction
  • Analysis: Fisher’s 2 × 2 Exact Test
  • Analysis: Rows × Columns (R × C) Contingency Tables
  • Conclusion
• 6: ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
  • Introduction
  • Scenario
  • Data
  • Analysis
    • Logic of ANOVA
    • How Is the Analysis Carried Out?
    • Interpretation
  • Assumptions of ANOVA
  • Conclusion
• 7: TWO-WAY ANALYSIS OF VARIANCE (ANOVA)
  • Introduction
  • Scenario
  • Data
  • Analysis
  • Interpretation
  • Factor A × Factor B Interaction Effect
  • Conclusion
• 8: REPEATED-MEASURES ANALYSIS OF VARIANCE (ANOVA)
  • Introduction
  • Scenario
  • Data
  • Analysis
  • Interpretation
  • Sphericity
  • Conclusion
• 9: MULTIPLE COMPARISONS AMONG TREATMENT MEANS
  • Introduction
  • Scenario
  • Data
  • Analysis
    • A Priori Comparisons
    • Post Hoc Tests
  • Interpretation
  • Conclusion
• 10: REGRESSION ANALYSIS
  • Introduction
  • Scenario
  • Data
  • Analysis: Regression Analysis
    • How Is the Analysis Carried Out?
    • Interpretation
  • Prediction of Y from X
  • Confidence Intervals
  • Testing the Slope
  • Conclusion
• 11: MULTIPLE LINEAR REGRESSION
  • Introduction
  • Scenario
  • Data
  • Analysis
  • Interpretation
  • Stepwise Regression
  • Polynomial Regression
  • Conclusion
• 12: CORRELATION ANALYSIS
  • Introduction
  • Scenario
  • Data
  • Analysis: Product-Moment Correlation
    • How Is the Analysis Carried Out?
    • Interpretation
  • Spearman’s Rank Correlation
  • Partial Correlation
  • Conclusion
• 13: WHAT ARE INDEPENDENT AND DEPENDENT VARIABLES?
  • Introduction
  • Variables in a Study
  • Conclusion
• 14: THE NATURE OF THE DATA
  • Introduction
  • Types of Data
  • Levels of Measurement
  • Conclusion
• 15: MEASURES OF VARIABILITY
  • Introduction
  • Scenario
  • Data
  • Analysis
  • Interpretation
  • Conclusion
• 16: WHAT ARE THE DEGREES OF FREEDOM?
  • Introduction
  • Degrees of Freedom in Statistical Tests
  • Conclusion
• 17: TYPE I AND TYPE II ERRORS
  • Introduction
  • Hypothesis Testing
  • Type I and Type II Errors
  • Power of a Test
  • Conclusion
• 18: FACTOR ANALYSIS
  • Introduction
  • Scenario
  • Data
  • Analysis: Factor Analysis
    • How Is the Analysis Carried Out?
    • Interpretation
  • Conclusion
• 19: LOGISTIC REGRESSION
  • Introduction
  • Scenario
  • Data
  • Analysis: Logistic Regression
    • How Is the Analysis Carried Out?
    • Interpretation
  • Conclusion
• 20: SURVIVAL ANALYSIS
  • Introduction
  • Scenario
  • Data
  • Analysis: Kaplan-Meier Method
    • How Is the Analysis Carried Out?
    • Interpretation
  • Log-Rank Test
  • Conclusion

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