“The strong selection of exercises at the end of each chapter is great.” — Kerri Nelson, Boston University

“. . . the perfect balance of complexity, brevity, practicality, and intellectually stimulating.” — Craig Byron, Mercer University

“The Fourth Edition adds depth to the material presented in the previous edition while keeping the information very accessible to students new to statistics. Changes to examples are particularly good in providing students with adequate practice and preparation for assessments.” — Philip Matich, Texas A&M University

"The book seems excellent. I appreciate the price and the introduction of R for students in this new edition. A good move." — Gregg Hartvigsen, SUNY, Geneseo

"I love this book! I especially like the ANOVA chapter where students can see the breakdown of ANOVA in steps. This really helps them understand what the process is and how the math works, sum of squares, etc." — Kimberly Dobrinski, University of Tampa

"This text is concise but gives sufficient detail for understanding and performing standard descriptive and inferential statistics. The examples are usually based on real data. The reasonable price is a contrast from most publishers." — John Messick, Missouri Southern State University

"By far the most student-friendly and effective statistics book out there for undergraduates. The clear writing, numerous case studies (with data included!), and straightforward math really help students understand statistics and not just memorize formulas." — Jeffrey Simmons, Mount St. Mary's University

1. Statistics and Sampling
What Is "Statistics" / Populations and Samples / Sampling Error: Precision, Accuracy, and Bias / Random Samples and Independence / How to Collect a Random Sample / Poor Sampling Designs / Conclusions

2. Variables and Data Management
Variables / Categorical Variables / Numerical Variables / Manipulating Numerical Data / Explanatory and Response Variables / Data Management

3. Summarizing Data in Tables and Graphs
Graphical Representation of Numerical Data / How to Create Meaningful Tables / How to Create Meaningful Graphs / Frequency Distributions and Probability Distributions / Frequency Distributions of Discrete Variables / Frequency Distributions of Continuous Variables / Histograms and Their Interpretation / Cumulative Frequency Distributions

4. Descriptive Statistics: Measures of Central Tendency and Dispersion
Sample Statistics and Population Parameters / Measures of Central Tendency / Measures of Dispersion

5. Probability and Discrete Probability Distributions
Classical and Empirical Probability / Division and Subtraction Rules / Counting Possibilities / The Multiplication Rule, Independence, and Conditional Probability / Conditional Probability: Tree Diagrams and Bayes' Theorem / Addition Rule and Mutually Exclusive Events / The Binomial Distribution / The Poisson Distribution

6. Statistical Inference and Hypothesis Testing
Statistical Inference / Statistical Hypotheses / Statistical Decisions and Their Potential Errors / Application: Steps in Testing a Statistical Hypothesis / Application of Statistics to Some Common Questions in Biology

7. Testing Hypotheses about Frequencies
The Chi-Square Goodness of Fit Test / The Chi-Square Test of Association / The Fisher Exact Probability Test

8. The Normal Distribution
The Normal Distribution and Its Properties / The Standard Normal Distribution and Use of Z-Scores / Sampling Distributions / Testing for Normality / Normal Approximation of the Binomial Distribution / Using the Normal Approximation for Inferences about Proportions

9. Inferences about a Single Population Mean: One-Sample and Paired Comparisons
Questions about the Mean from a Single Population / The t-Distribution / Estimation: Confidence Interval for ? / Reporting a Sample Mean and Its Variation / Hypothesis Concerning a Single Population Mean / Matched-Pairs Tests: Think Differences / The Paired t-Test / How to Proceed If the Normality Assumption Is Violated / Nonparametric Tests for Two Related Samples

10. Inferences Concerning Two Population Means
The t-Test for Two Independent Samples / Confidence Interval for the Difference between Two Population Means / A Nonparametric Test for Two Independent Samples: The Mann-Whitney U Test / Power of the Test: How Large a Sample Is Sufficient? / Review: What Is the Appropriate Statistical Test?

11. Inferences Concerning Means from Multiple Populations: ANOVA
The Rationale of ANOVA: An Illustration / The Assumptions of ANOVA / Fixed-Effects ANOVA / Testing the Assumptions of ANOVA / Remedies for Failed Assumptions

12. More ANOVA: Randomized Block and Factorial Designs
The Randomized Block Design / The Friedman Test / The Factorial Design / Other ANOVA Designs

13. Associations between Continuous Variables: Correlation
Associations or Modeling: When to Use Correlation or Regression / The Pearson Correlation Coefficient / A Correlation Matrix / Nonparametric Correlation Analysis (Spearman's r)

14. Modeling the Effects of One Continuous Variable on Another: Regression Analysis
Fundamentals of Simple Linear Regression / Conceptualizing Regression Analysis / Estimating the Regression Parameters / Testing the Significance of the Regression Equation / Confidence Interval for ? / The Coefficient of Determination (r²) / Regression Analysis: Using Survey Data / Predicting y from x / Checking Assumptions and Remedies for Their Failure / Advanced Regression Techniques

15. Selecting Appropriate Statistical Procedures
General Process for a Complete Data Analysis / Choosing the Appropriate Statistical Test / Some Statistical Methods Not Covered in Previous Chapters

16. Experimental Design
Research Questions, Surveys, and Experiments / How to Avoid Bias While Testing for Treatment Effects / How to Reduce Sampling Error and Its Influences / Seek Advice from Experts

Appendix A: Statistical Tables
Appendix B: Answers to Practice Exercises
Appendix C: Installing and Running R
Appendix D: Literature Cited