3rd Edition

# Introduction to Statistics for Biology

296 Pages 78 B/W Illustrations
by Chapman & Hall

296 Pages
by Chapman & Hall

Also available as eBook on:

Even though an understanding of experimental design and statistics is central to modern biology, undergraduate and graduate students studying biological subjects often lack confidence in their numerical abilities. Allaying the anxieties of students, Introduction to Statistics for Biology, Third Edition provides a painless introduction to the subject while demonstrating the importance of statistics in contemporary biological studies.

New to the Third Edition

• More detailed explanation of the ideas of elementary probability to simplify the rationale behind hypothesis testing, before moving on to simple tests
• An emphasis on experimental design and data simulation prior to performing an experiment
• A general template for carrying out statistical tests from hypothesis to interpretation
• Worked examples and updated Minitab analyses and graphics

Using Minitab throughout to present practical examples, the authors emphasize the interpretation of computer output. With its nontechnical approach and practical advice, this student-friendly introductory text lays the foundation for the advanced study of statistical analysis.
• PREFACE

HOW LONG IS A WORM?
Introduction
Sampling a Population
The Normal Distribution
Probability
Continuous Measurements-Worms Again
Expressing Variability

CONFIDENCE INTERVALS
The Importance of Confidence Intervals
Calculating Confidence Intervals
Another Way of Looking At It
One- and Two-Tailed Tests
The Other Side of the Coin-Type II Errors
Recap-Hypothesis Testing
A Complication
Testing Fish with t
Minitab Does a One-Sample t-Test
95% CI for Worms
Anatomy of Test Statistics

COMPARING THINGS: TWO SAMPLE TESTS
A Simple Case
Matched-Pairs t-Test
Another Example-Testing Twin Sheep
Independent Samples: Comparing Two Populations
Calculation of Independent Samples t-Test
One- and Two-Tailed Tests-A Reminder
Minitab Carries Out a Two-Sample t-Test
Pooling the Variances?

PLANNING AN EXPERIMENT
Principles of Sampling
Principles of Experimental Design
Recording Data and Simulating an Experiment

PARTITIONING VARIATION AND CONSTRUCTING A MODEL
It's Simple
… But Not That Simple
The Example: Field Margins in Conservation
The Idea of a Statistical Model
Laying Out the Experiment
Sources of Variation: Random Variation
The Model

ANALYZING YOUR RESULTS: IS THERE ANYTHING THERE?
Is Spider Abundance Affected by Treatment?
Why Not Use Multiple t-Tests?
ANOVA for a Wholly Randomized Design
Comparing the Sources of Variation
The Two Extremes of Explanation: All or Nothing
The ANOVA Table
Testing Our Hypothesis
Including Blocks: Randomized Complete Block Designs
Analyzing the Spider Data Set in Minitab
The Assumptions behind ANOVA and How to Test Them
Another Use for the F-Test: Testing Homogeneity of Variance

INTERPRETING YOUR ANALYSIS: FROM HYPOTHESIS TESTING TO BIOLOGICAL MEANING
Treatment Means and Confidence Intervals
Difference between Two Treatment Means
Getting More Out of an Experiment: Factorial Designs and Interactions
Getting More Out of the Analysis: Using the Factorial Design to Ask More Relevant Questions
Interactions
Adding Blocking to the Factorial Analysis
How to Interpret Interaction Plots: The Plant Hormone Experiment
Loss of Data and Unbalanced Experiments
Limitations of ANOVA and the General Linear Model (GLM)

RELATING ONE VARIABLE TO ANOTHER
Correlation
Calculating the Correlation Coefficient, and a New Idea: Covariance
Regression
Linear Regression
The Model
Interpreting Hypothesis Tests in Regression
A Further Example of Linear Regression
Assumptions
The Importance of Plotting Observations
Confidence Intervals
Standard Error of Prediction (Prediction Interval)
Caution in the Interpretation of Regression and Correlation

CATEGORICAL DATA
The Chi-Squared Goodness-of-Fit Test
A More Interesting Example: Testing Genetic Models
Contingency Analysis: Chi-Squared Test of Proportions
A Further Example of a Chi-Squared Contingency Test
Beyond Two-Dimensional Tables: The Likelihood Ratio Chi-Square

NONPARAMETRIC TESTS
Introduction
Basic Ideas
A Taxonomy of Tests
Single-Sample Tests
Matched-Pairs Tests
Independent Samples
Two Quantitative Variables: Spearman's Rank Correlation
Why Bother with Parametric Tests?

Choosing a Topic and a Supervisor
Common Mistakes
General Principles of Experimental Design and Execution
Analyzing Your Data and Writing the Report
Structure
The First Draft
Illustrating Results

APPENDIX A: AN INTRODUCTION TO MINITAB
Conventions Used in This Book
Starting Up
Help
Data Entry
Looking at the Worms Data
Updating Graphs
Stacking and Unstacking-A Useful Trick
Looking Up Probabilities
Report Writer
The Minitab Command Line

APPENDIX B: STATISTICAL POWER AND SAMPLE SIZE

APPENDIX C: STATISTICAL TABLES

APPENDIX D: REFERENCES AND FURTHER READING

APPENDIX E: STATISTICAL TESTS

INDEX

### Biography

Trudy A. Watt, Robin H. McCleery, Tom Hart

"With its non-technical approach and practical advice, this accessible to students introductory text lays the foundation for more advanced study of statistical analysis and biometry. I strongly recommend this excellent text to all undergraduate students in the biological sciences."
Journal of the Royal Statistical Society