The Analysis of Biological Data
Third Edition ©2020 Michael C. Whitlock; Dolph Schluter Formats: Achieve, E-book, Print
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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.
Table of Contents
Data Set files needed to complete exercises and examples in the book can be downloaded from the open-access student companion website. Data Sets are available in Excel, CSV, Minitab, R, JMP, TI, SPSS, PC-Text, and Mac-Text formats.
PART 1 INTRODUCTION TO STATISTICS
1.0 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 Fishers 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 cant 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 Spearmans 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
Product Updates
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.
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.
Table of Contents
Data Set files needed to complete exercises and examples in the book can be downloaded from the open-access student companion website. Data Sets are available in Excel, CSV, Minitab, R, JMP, TI, SPSS, PC-Text, and Mac-Text formats.
PART 1 INTRODUCTION TO STATISTICS
1.0 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 Fishers 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 cant 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 Spearmans 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
Product Updates
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.
Practical data analysis using real biological examples
Now available with Macmillan’s new online learning platform Achieve, Analysis of Biological Data is beloved by instructors and students for providing 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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Here’s why educators who use Achieve would recommend it to their peers.
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Test Bank for The Analysis of Biological Data
Michael C. Whitlock; Dolph Schluter | Third Edition | ©2020 | ISBN:9781319226268
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If you’re a verified instructor, you can request a free sample of our courseware, e-book, or print textbook to consider for use in your courses. Only registered and verified instructors can receive free print and digital samples, and they should not be sold to bookstores or book resellers. If you don't yet have an existing account with Macmillan Learning, it can take up to two business days to verify your status as an instructor. You can request a free sample from the right side of this product page by clicking on the "Request Instructor Sample" button or by contacting your rep. Learn more.
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Sometimes also referred to as a spiral-bound or binder-ready textbook, loose-leaf textbooks are available to purchase. This three-hole punched, unbound version of the book costs less than a hardcover or paperback book.
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Achieve (full course) includes our complete e-book, as well as online quizzing tools, multimedia assets, and iClicker active classroom manager.
Most Achieve Essentials courses do not include our e-books and adaptive quizzing.
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Achieve (full course) includes our complete e-book, as well as online quizzing tools, multimedia assets, and iClicker active classroom manager.
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We can help! Contact your representative to discuss your specific needs for your course. If our off-the-shelf course materials don’t quite hit the mark, we also offer custom solutions made to fit your needs.
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The Analysis of Biological Data
Now available with Macmillan’s new online learning platform Achieve, Analysis of Biological Data is beloved by instructors and students for providing 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.
These materials are owned by Macmillan Learning or its licensors and are protected by United States copyright law. They are being provided solely for evaluation purposes only by instructors who are considering adopting Macmillan Learning's textbooks or online products for use by students in their courses. These materials may not be copied, distributed, sold, shared, posted online, or used, in print or electronic format, except in the limited circumstances set forth in the Macmillan Learning Terms of Use and any other reproduction or distribution is illegal. These materials may not be made publicly available under any circumstances. All other rights reserved. © 2020 Macmillan Learning.
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