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# The Analysis of Biological Data

## Third Edition| ©2020 Michael C. Whitlock; Dolph Schluter

Now available with Macmillan’s new online learning platform Achieve, *Analysis of Biological Data* provides a practical foundation of statistics for biology students. Every chapter has several biological or medical examples related to key statistics concepts, and each example is prefaced b...

Now available with Macmillan’s new online learning platform Achieve, *Analysis of Biological Data* provides a practical foundation of statistics for biology students. Every chapter has several biological or medical examples related to key statistics concepts, and each example is prefaced by a substantial description of the biological setting. The emphasis on real and interesting examples carries into the problem sets where students have a wealth of practice problems based on real data.

The third edition features over 200 new examples and problems. These include new calculation practice problems, which guide the student step by step through the methods, and a greater number of examples and topics that come from medical and human health research. Every chapter has been carefully edited for even greater clarity and ease of use, and is easier than ever to access through Achieve.

Achieve for *Analysis of Biological Data* connects the problem-solving approach and real world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface.

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## Practical data analysis using real biological examples

Now available with Macmillan’s new online learning platform Achieve, *Analysis of Biological Data* provides a practical foundation of statistics for biology students. Every chapter has several biological or medical examples related to key statistics concepts, and each example is prefaced by a substantial description of the biological setting. The emphasis on real and interesting examples carries into the problem sets where students have a wealth of practice problems based on real data.

The third edition features over 200 new examples and problems. These include new calculation practice problems, which guide the student step by step through the methods, and a greater number of examples and topics that come from medical and human health research. Every chapter has been carefully edited for even greater clarity and ease of use, and is easier than ever to access through Achieve.

Achieve for *Analysis of Biological Data* connects the problem-solving approach and real world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface.

Features

**Achieve Online Homework**

**Over 3,000 homework questions**of varying difficulty, Bloom’s level, and question type. Every homework question includes a hint, answer-specific feedback, and a fully worked solution. Question types in Achieve include- Multiple choice
- Ranking
- Sorting
- Numeric entry
- Multi-part questions
- Questions with algorithmically regenerating values
**LearningCurve adaptive quizzing**puts the concept of "testing to learn" into action, motivating students to engage with the text's content to identify areas of proficiency. Easy-to-use reporting tools help teachers pinpoint areas to focus on in class.

\- A mobile,
**interactive e-book**, powered by VitalSource, allows students to highlight and take notes, print select pages, and have the text read aloud to them. - Over 140
**StatTutors**--multimedia tutorials that explore important concepts and procedures in a presentation that combines video, audio, and interactive features--are assignable, gradeable, and organized by chapter. **Applet Activities**are visual interactives that allow students to manipulate data and variables in calculations and see the results graphically. Applets also contain assessment questions to test students’ comprehension.**Videos**provide additional exposure to key concepts and examples. Videos are narrated and close-captioned. Video types include- Whiteboard-style problem-solving videos
- StatTutor video lessons
- Animated lectures and documentary-style videos that illustrate real world scenarios involving statistics
**EESEE**(Electronic Encyclopedia of Statistical Examples and Exercises)**Case Studies**, developed by the Ohio State University Statistics Department, teach students to apply their statistical skills by exploring actual case studies using real data.**Video Technology Manuals**are brief instructional videos that provide basic introductions for working with CrunchIt!, Excel, SPSS, TI-83/84 calculators, JMP, Minitab, R, and RCmdr.

** Statistical software options**

**CrunchIt!,**Macmillan’s proprietary online statistical software powered by R, handles every computation and graphing function an introductory statistics student needs. CrunchIt! is preloaded with data sets, and it allows editing and importing additional data.- Students also receive access to
**JMP**Student Edition (developed by SAS). With the student edition of JMP, students handle large data, visualizations, and analysis for which the professional version is renowned. Additionally, text-specific data sets are included for download. - For other statistical software, Achieve includes data sets, including those for
- Excel
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- Mac-text & PC-text
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New to This Edition

**Achieve online homework.** Based on research from Macmillan’s Learning Science team, Achieve marries the powerful, tutorial-style assessment of Sapling Learning with rich book-specific resources in one easy-to-use, accessible platform.

**New practice and assignment problems** to every chapter covering all major concepts and skills.

Integrated online activities with the text for learning the R statistical software environment.

**New chapter added on survival analysis**, a vital topic in biostatistics.

**New instructor resources**, including answers to assignment problems and R Code labs, are available at whitlockschluter3e.zoology.ubc.ca.

Look Inside

**
The Analysis of Biological Data**

Third Edition| ©2020

Michael C. Whitlock; Dolph Schluter

# Digital Options

## Achieve

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## SaplingPlus

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## E-book

Read online (or offline) with all the highlighting and notetaking tools you need to be successful in this course.

## Table of Contents

PART 1 INTRODUCTION TO STATISTICS1.0 Statistics and samples

1.1 What is statistics?

1.2 Sampling populations

1.3 Types of data and variables

1.4 Frequency distributions and probability distributions

1.5 Types of studies

1.6 Summary

Interleaf 1 Correlation does not require causation

2.0 Displaying data

2.1 Guidelines for effective graphs

2.2 Showing data for one variable

2.3 Showing association between two variables and differences between groups

2.4 Showing trends in time and space

2.5 How to make good tables

2.6 How to make data files

2.7 Summary

3.0 Describing data

3.1 Arithmetic mean and standard deviation

3.2 Median and interquartile range

3.3 How measures of location and spread compare

3.4 Cumulative frequency distribution

3.5 Proportions

3.6 Summary

3.7 Quick Formula Summary

4.0 Estimating with uncertainty

4.1 The sampling distribution of an estimate

4.2 Measuring the uncertainty of an estimate

4.3 Confidence intervals

4.4 Error bars

4.5 Summary

4.6 Quick Formula Summary

Interleaf 2 Pseudoreplication

5.0 Probability

5.1 The probability of an event

5.2 Venn Diagrams

5.3 Mutually exclusive events

5.4 Probability distributions

5.5 Either this or that: adding probabilities

5.6 Independence and the multiplication rule

5.7 Probability trees

5.8 Dependent events

5.9 Conditional probability and Bayes' theorem

5.10 Summary

6.0 Hypothesis testing

6.1 Making and using hypotheses

6.2 Hypothesis testing: an example

6.3 Errors in hypothesis testing

6.4 When the null hypothesis is not rejected

6.5 One-sided tests

6.6 Hypothesis testing versus confidence intervals

6.7 Summary

Intereaf 3 Why statistical significance is not the same as biological importance

PART 2 PROPORTIONS AND FREQUENCIES

7.0 Analyzing proportions

7.1 The binomial distribution

7.2 Testing a proportion: the binomial test

7.3 Estimating proportions

7.4 Deriving the binomial distribution

7.5 Summary

7.6 Quick Formula Summary

Interleaf 4 Biology and the history of statistics

8.0 Fitting probability models to frequency data

8.1 X^2 goodness-of-fit test: the proportional model

8.2 Assumptions of the X^2 goodness-of-fit test

8.3 Goodness-of-fit tests when there are only two categories

8.4 Random in space or time: the Poisson distribution

8.5 Summary

8.6 Quick Formula Summary

Interleaf 5 Making a plan

9.0 Contingency analysis: Associations between categorical variables

9.1 Associating two categorical variables

9.2 Estimating association in 2 × 2 tables: relative risk

9.3 Estimating association in 2x2 tables: the odds ratio

9.4 The x^2 contingency test

9.5 Fisher's exact test

9.6 Summary

9.7 Quick Formula Summary

PR1 Review Problems 1

PART 3 COMPARING NUMERICAL VALUES

10.0 The normal distribution

10.1 Bell-shaped curves and the normal distribution

10.2 The formula for the normal distribution

10.3 Properties of the normal distribution

10.4 The standard normal distribution and statistical tables

10.5 The normal distribution of sample means

10.6 Central limit theorem

10.7 Normal approximation to the binomial distribution

10.8 Summary

10.9 Quick Formula Summary

Interleaf 6 Controls in medical studies

11.0 Inference for a normal population

11.1 The t-distribution for sample means

11.2 The confidence interval for the mean of a sample distribution

11.3 The one-sample t-test

11.4 Assumptions of the one-sample t-test

11.5 Estimating the standard deviation and variance of a normal population

11.6 Summary

11.7 Quick Formula Summary

12.0 Comparing two means

12.1 Paired sample versus two independent samples

12.2 Paired comparison of means

12.3 Two-sample comparison of means

12.4 Using the correct sampling units

12.5 The fallacy of indirect comparison

12.6 Interpreting overlap of confidence intervals

12.7 Comparing variances

12.8 Summary

12.9 Quick Formula Summary

Interleaf 7 Which test should I use?

13.0 Handling violations of assumptions

13.1 Detecting deviations from normality

13.2 When to ignore violations of assumptions

13.3 Data transformations

13.4 Nonparametric alternatives to one-sample and paired t-tests

13.5 Comparing two groups: the Mann-Whitney U-test

13.6 Assumptions of nonparametric tests

13.7 Type I and Type II error rates of nonparametric methods

13.8 Permutation tests

13.9 Summary

13.10 Quick Formula Summary

RP2 Review Problems 2

14.0 Designing experiments

14.1 Lessons from clinical trials

14.2 How to reduce bias

14.3 How to reduce the influence of sampling error

14.4 Experiments with more than one factor

14.5 What if you can't do experiments?

14.6 Choosing a sample size

14.7 Summary

14.8 Quick Formula Summary

Interleaf 8 Data dredging

15.0 Comparing means of more than two groups

15.1 The analysis of variance

15.2 Assumptions and alternatives

15.3 Planned comparisons

15.4 Unplanned comparisons

15.5 Fixed and random effects

15.6 ANOVA with randomly chosen groups

15.7 Summary

15.8 Quick Formula Summary

Interleaf 9 Experimental and statistical mistakes

PART 4 REGRESSION AND CORRELATION

16.0 Correlation between numerical variables

16.1 Estimating a linear correlation coefficient

16.2 Testing the null hypothesis of zero correlation

16.3 Assumptions

16.4 The correlation coefficient depends on the range

16.5 Spearman's rank correlation

16.6 The effects of measurement error on correlation

16.7 Summary

16.8 Quick Formula Summary

Interleaf 10 Publication bias

17.0 Regression

17.1 Linear Regression

17.2 Confidence in predictions

17.3 Testing hypotheses about a slope

17.4 Regression toward the mean

17.5 Assumptions of regression

17.6 Transformations

17.7 The effects of measurement error on regression

17.8 Regression with nonlinear relationships

17.9 Logistic regression: fitting a binary response variable

17.10 Summary

17.11 Quick Formula Summary

Interleaf 11 Meta-analysis

RP3 Review Problems 3

PART 5 MODERN STATISTICAL METHODS

18.0 Multiple explanatory variables

18.1 ANOVA and linear regression are linear models

18.2 Analyzing experiments with blocking

18.3 Analyzing factorial designs

18.4 Adjusting for the effects of a covariate

18.5 Assumptions of general linear models

18.6 Summary

Interleaf 12 Using species as data points

19.0 Computer-intensive methods

19.1 Hypothesis testing using simulation

19.2 Bootstrap standard errors and confidence intervals

19.3 Summary

20.0 Likelihood

20.1 What is the likelihood?

20.2 Two uses of likelihood in biology

20.3 Maximum likelihood estimation

20.4 Versatility of maximum likelihood estimation

20.5 Log-likelihood ratio test

20.6 Summary

20.7 Quick Formula Summary

21.0 Survivorship analysis

21.1 Survival curves

21.2 Comparing two survival curves

21.3 Summary

21.4 Quick Formula Summary

BACK MATTER

Statistical tables

Literature cited

Answers to practice problems

Index

## Authors

### Michael C. Whitlock

Michael Whitlock is an evolutionary biologist and population geneticist. He is a professor of zoology at the University of British Columbia, where he has taught statistics to biology students since 1995. Whitlock is known for his work on the spatial structure of biological populations, genetic drift, and the genetics of adaptation. He has worked with fungus beetles, rhinos, and fruit flies; mathematical theory; and statistical genetics. He is a fellow of the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science. He has been Editor-in-Chief of The American Naturalist and on the editorial boards of nine scientific journals.

### Dolph Schluter

Dolph Schluter is a Professor and Canada Research Chair in the Zoology Department and Biodiversity Research Center at the University of British Columbia. He is known for his research on the ecology and evolution of Galapagos finches and threespine stickleback. He is a fellow of the Royal Societies of Canada and London and a foreign associate of the Academy of Arts and Sciences.

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