The Analysis of Biological Data

Name: The Analysis of Biological Data
Price: 113.99 USD
Availability: InStock
ISBN: 9781319226237

by Michael C. Whitlock, Dolph Schluter

3rd Edition

Publisher: W.H. Freeman & Company

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Life Sciences

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Book Details

Print ISBN	9781319226237
eText ISBN	9781319226299
Publisher	W.H. Freeman & Company
Publishing Year	2020
Edition	3rd Edition
Language	English
Pages	848

Modern life sciences generate massive volumes of complex empirical evidence, making rigorous statistical literacy more critical than ever for aspiring researchers. The Analysis of Biological Data 3rd Edition addresses this modern educational demand by transforming abstract mathematical theory into an intuitive, practical toolkit. Rather than presenting biostatistics as a collection of dry, intimidating formulas, this text connects statistical principles directly to the vibrant reality of scientific discovery. By grounding every single concept in genuine biological scenarios, it helps students overcome quantitative anxiety and develop a lasting conceptual framework for data interpretation. This approach ensures that learners appreciate the vital role that statistics plays in solving real-world ecological and medical mysteries.

Throughout this comprehensive volume, the authors maintain a strict focus on real-world application, utilizing actual data sets from published scientific literature. The scope spans foundational probability and descriptive statistics before advancing to complex multi-group comparisons, regression models, and non-parametric analyses. By prioritizing intuitive understanding over rote calculation, the text teaches students how to choose the correct analytical tests, recognize potential confounding variables, and interpret results within their proper ecological or medical contexts. This practical perspective ensures that learners do not just perform calculations but actually think like active, critical scientists who can evaluate the validity of experimental designs.

Designed for introductory and intermediate biostatistics courses, this edition introduces refined pedagogical tools, updated exercises, and clearer explanations of challenging concepts. Instructors worldwide adopt The Analysis of Biological Data 3rd Edition because of its unmatched ability to engage students through narrative-driven problem sets. For those seeking flexible digital study options, accessing The Analysis of Biological Data 3rd Edition PDF allows students to seamlessly integrate their reading with modern computational tools like R. Ultimately, this textbook serves as an indispensable guide for anyone preparing to navigate the data-rich landscape of contemporary biological research, making it a staple in modern science curricula.

Chapter 1: Statistics and Samples
- • 1.1 Empirical evidence and biological questions
- • 1.2 Populations and samples
- • 1.3 Types of variables
- • 1.4 Experimental versus observational studies
Chapter 2: Displaying Data
- • 2.1 Frequency distributions and bar graphs
- • 2.2 Histograms and relative frequency
- • 2.3 Association between categorical variables
- • 2.4 Displaying relationships between numerical variables
Chapter 3: Describing Data
- • 3.1 Arithmetic mean and median
- • 3.2 Measures of variance and standard deviation
- • 3.3 Percentiles and box plots
- • 3.4 Cumulative frequency distributions
Chapter 4: Estimating with Uncertainty
- • 4.1 Sampling distributions and standard error
- • 4.2 Confidence intervals
- • 4.3 Standard error of the mean
Chapter 5: Probability
- • 5.1 Probability of an event
- • 5.2 Mutually exclusive events
- • 5.3 Independent events and multiplication rule
- • 5.4 Probability trees and Bayes' theorem
Chapter 6: Hypothesis Testing
- • 6.1 Null and alternative hypotheses
- • 6.2 Test statistics and null distributions
- • 6.3 Type I and Type II errors
- • 6.4 One-tailed versus two-tailed tests
Chapter 7: Analyzing Proportions
- • 7.1 Binomial distribution
- • 7.2 Binomial test
- • 7.3 Estimating proportions with confidence intervals
Chapter 8: Fitting Probability Models to Frequency Data
- • 8.1 Chi-square goodness-of-fit test
- • 8.2 Assumptions of chi-square tests
- • 8.3 Fitting Poisson distributions
Chapter 9: Contingency Analysis: Association Between Categorical Variables
- • 9.1 Chi-square test of independence
- • 9.2 Fisher's exact test
- • 9.3 Odds ratios and relative risk
Chapter 10: The Normal Distribution
- • 10.1 Properties of the normal distribution
- • 10.2 Standard normal distribution and Z-scores
- • 10.3 Central limit theorem
Chapter 11: One-Sample t-Test and Paired t-Test
- • 11.1 Student's t-distribution
- • 11.2 One-sample t-test
- • 11.3 Paired t-test design and analysis
- • 11.4 Assumptions of t-tests
Chapter 12: Two-Sample t-Test
- • 12.1 Two-sample t-test for independent samples
- • 12.2 F-test of equal variances
- • 12.3 Welch's t-test for unequal variances
- • 12.4 Mann-Whitney U test as non-parametric alternative
Chapter 13: Comparing Means of More Than Two Groups (ANOVA)
- • 13.1 Single-factor analysis of variance
- • 13.2 Partitioning sum of squares
- • 13.3 Tukey-Kramer honestly significant difference test
- • 13.4 Kruskal-Wallis non-parametric test
Chapter 14: Correlation Between Numerical Variables
- • 14.1 Pearson product-moment correlation
- • 14.2 Testing hypothesis of zero correlation
- • 14.3 Spearman's rank correlation
Chapter 15: Regression
- • 15.1 Method of least squares
- • 15.2 Simple linear regression
- • 15.3 Testing the slope of a regression line
- • 15.4 Assumptions and residual analysis

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Table of Contents

Chapter 1: Statistics and Samples
- • 1.1 Empirical evidence and biological questions
- • 1.2 Populations and samples
- • 1.3 Types of variables
- • 1.4 Experimental versus observational studies
Chapter 2: Displaying Data
- • 2.1 Frequency distributions and bar graphs
- • 2.2 Histograms and relative frequency
- • 2.3 Association between categorical variables
- • 2.4 Displaying relationships between numerical variables
Chapter 3: Describing Data
- • 3.1 Arithmetic mean and median
- • 3.2 Measures of variance and standard deviation
- • 3.3 Percentiles and box plots
- • 3.4 Cumulative frequency distributions
Chapter 4: Estimating with Uncertainty
- • 4.1 Sampling distributions and standard error
- • 4.2 Confidence intervals
- • 4.3 Standard error of the mean
Chapter 5: Probability
- • 5.1 Probability of an event
- • 5.2 Mutually exclusive events
- • 5.3 Independent events and multiplication rule
- • 5.4 Probability trees and Bayes' theorem
Chapter 6: Hypothesis Testing
- • 6.1 Null and alternative hypotheses
- • 6.2 Test statistics and null distributions
- • 6.3 Type I and Type II errors
- • 6.4 One-tailed versus two-tailed tests
Chapter 7: Analyzing Proportions
- • 7.1 Binomial distribution
- • 7.2 Binomial test
- • 7.3 Estimating proportions with confidence intervals
Chapter 8: Fitting Probability Models to Frequency Data
- • 8.1 Chi-square goodness-of-fit test
- • 8.2 Assumptions of chi-square tests
- • 8.3 Fitting Poisson distributions
Chapter 9: Contingency Analysis: Association Between Categorical Variables
- • 9.1 Chi-square test of independence
- • 9.2 Fisher's exact test
- • 9.3 Odds ratios and relative risk
Chapter 10: The Normal Distribution
- • 10.1 Properties of the normal distribution
- • 10.2 Standard normal distribution and Z-scores
- • 10.3 Central limit theorem
Chapter 11: One-Sample t-Test and Paired t-Test
- • 11.1 Student's t-distribution
- • 11.2 One-sample t-test
- • 11.3 Paired t-test design and analysis
- • 11.4 Assumptions of t-tests
Chapter 12: Two-Sample t-Test
- • 12.1 Two-sample t-test for independent samples
- • 12.2 F-test of equal variances
- • 12.3 Welch's t-test for unequal variances
- • 12.4 Mann-Whitney U test as non-parametric alternative
Chapter 13: Comparing Means of More Than Two Groups (ANOVA)
- • 13.1 Single-factor analysis of variance
- • 13.2 Partitioning sum of squares
- • 13.3 Tukey-Kramer honestly significant difference test
- • 13.4 Kruskal-Wallis non-parametric test
Chapter 14: Correlation Between Numerical Variables
- • 14.1 Pearson product-moment correlation
- • 14.2 Testing hypothesis of zero correlation
- • 14.3 Spearman's rank correlation
Chapter 15: Regression
- • 15.1 Method of least squares
- • 15.2 Simple linear regression
- • 15.3 Testing the slope of a regression line
- • 15.4 Assumptions and residual analysis