Fundamentals of Statistics, 6th edition

Published by Pearson (April 9, 2021) © 2022

  • Michael Sullivan Joliet Junior College


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For courses in Introductory Statistics.

Helps students put statistical concepts together

Mike Sullivan draws on his passion for both statistics and teaching to show that statistics is connected, not only in concepts but to the world at large. Used first with his own students, Fundamentals of Statistics, 6th Edition (the brief version of Statistics: Informed Decisions Using Data) pulls ideas and strategies from his classroom into a wealth of new exercises, examples and much more. Utilizing the latest statistical software, students can focus on building understanding rather than memorizing formulas. All resources were created by Mike for his classroom and are available to help all students succeed.

Hallmark features of this title

  • Putting It Together connects concepts from different chapters to show statistics as a whole, rather than a series of disconnected procedures.
  • Making an Informed Decision chapter openers pose a question, then present the statistical concept needed for prudent decision-making.
  • Case Studies conclude each chapter and help students apply their knowledge. 
  • Preparing for this Section quizzes with page numbers for quick review verify that students have the prerequisite knowledge for the next section.
  • Retain Your Knowledge problems help students recall skills learned earlier, so the material will be fresh for the final exam. These appear periodically at the end-of-section exercises.
  • Now Work problems follow most examples so that students can practice concepts.

New and updated features of this title

  • Over 350 new and updated exercises throughout; students also are encouraged to write a few sentences that explain the results of their statistical analysis.
  • Over 100 new and updated examples throughout follow the Problem, Approach, Solution approach to statistical problem solving. Problem lays out the scenario of the example; Approach provides insight into the thought process behind the methodology used to solve the problem; Solution goes through the solution utilizing the methodology suggested in the approach. 
  • New Threaded Tornado Problems throughout present a single, large data set measuring various variables on all tornadoes that struck the United States in 2017.
  • Optional Simulation & Randomization sections reflect the current approach of using simulation and randomization for hypothesis testing; available online only.
  • Classroom Notes can be used to deliver lectures to students. Students can print these notes out and bring them to class, which encourages good note-taking and allows them to focus on the concepts. The examples and activities in the classroom notes are different from those in the text and Instructor's Resource Guide. 
  • Additionaldata sets are larger and more complex to support modern data analysis. 

Features of MyLab Statistics for the 6th Edition

  • Updated Integrated Review offers corequisite support or can help underprepared students. It provides embedded and personalized review of prerequisite topics, selected by the author, within relevant chapters. In this revision, select topics have been added to provide further review. All assignments are premade and editable for instructors to assign.
  • Updated video program features new video types to provide more support to students:
    • New lightboard videos featuring the author develop statistical concepts for students, and new Excel video solutions are available for any example in which Excel may be used to obtain statistical results. 
    • Updated animated videos explain concepts or tie material learned earlier in the course with the upcoming chapter or section. Additional animated videos have been added.
  • New and updated MyLab problems: Written by the author, these utilize real data that is randomly generated from a larger data set. Sullivan also wrote new, assignable StatCrunch® applet exercises that allow students to explore statistical concepts. 
  • Author-specific applets can be used in the classroom or as part of a project featured in the Activity Workbook, asking students to manipulate data to better understand concepts. New applet exercises in the MyLab encourage this exploration. 
  • Preface to the Instructor
  • Resources for Success
  • Applications Index


  • 1. Data Collection
    • 1.1 Introduction to the Practice of Statistics
    • 1.2 Observational Studies versus Designed Experiments
    • 1.3 Simple Random Sampling
    • 1.4 Other Effective Sampling Methods
    • 1.5 Bias in Sampling
    • 1.6 The Design of Experiments
    • Chapter 1 Review
    • Chapter Test
    • Making an Informed Decision: What College Should I Attend?


  • 2. Summarizing Data in Tables and Graphs
    • 2.1 Organizing Qualitative Data
    • 2.2 Organizing Quantitative Data
    • 2.3 Graphical Misrepresentations of Data
    • Chapter 2 Review
    • Chapter Test
    • Making an Informed Decision: Tables or Graphs?
  • 3. Numerically Summarizing Data
    • 3.1 Measures of Central Tendency
    • 3.2 Measures of Dispersion
    • 3.3 Measures of Central Tendency and Dispersion from Grouped Data
    • 3.4 Measures of Position and Outliers
    • 3.5 The Five-Number Summary and Boxplots
    • Chapter 3 Review
    • Chapter Test
    • Making an Informed Decision: What Car Should I Buy?
  • 4. Describing the Relation between Two Variables
    • 4.1 Scatter Diagrams and Correlation
    • 4.2 Least-Squares Regression
    • 4.3 The Coefficient of Determination
    • 4.4 Contingency Tables and Association
    • Chapter 4 Review
    • Chapter Test
    • Making an Informed Decision: Relationships among Variables on a World Scale


  • 5. Probability
    • 5.1 Probability Rules
    • 5.2 The Addition Rule and Complements
    • 5.3 Independence and the Multiplication Rule
    • 5.4 Conditional Probability and the General Multiplication Rule
    • 5.5 Counting Techniques
    • 5.6 Simulating Probability Experiments
    • 5.7 Putting It Together: Which Method Do I Use?
    • Chapter 5 Review
    • Chapter Test
    • Making an Informed Decision: The Effects of Drinking and Driving
  • 6. Discrete Probability Distributions
    • 6.1 Discrete Random Variables
    • 6.2 The Binomial Probability Distribution
    • Chapter 6 Review
    • Chapter Test
    • Making an Informed Decision: Should We Convict?
  • 7. The Normal Probability Distribution
    • 7.1 Properties of the Normal Distribution
    • 7.2 Applications of the Normal Distribution
    • 7.3 Assessing Normality
    • 7.4 The Normal Approximation to the Binomial Probability Distribution
    • Chapter 7 Review
    • Chapter Test
    • Making an Informed Decision: Stock Picking


  • 8. Sampling Distributions
    • 8.1 Distribution of the Sample Mean
    • 8.2 Distribution of the Sample Proportion
    • Chapter 8 Review
    • Chapter Test
    • Making an Informed Decision: How Much Time Do You Spend in a Day . . . ?
  • 9. Estimating the Value of a Parameter
    • 9.1 Estimating a Population Proportion
    • 9.2 Estimating a Population Mean
    • 9.3 Putting It Together: Which Method Do I Use?
    • 9.4 Estimating a Population Standard Deviation (eText)
    • 9.5 Estimating with Bootstrapping (eText)
    • Chapter 9 Review
    • Chapter Test
    • Making an Informed Decision: How Much Should I Spend for this House?
  • 10. Hypothesis Tests Regarding a Parameter
    • 10.1 The Language of Hypothesis Testing
    • 10.2 Hypothesis Tests for a Population Proportion
    • 10.2A Using Simulation to Perform Hypothesis Tests on a Population Proportion (eText)
    • 10.2B Hypothesis Tests for a Population Proportion Using the Normal Model (eText)
    • 10.3 Hypothesis Tests for a Population Mean
    • 10.3A Using Simulation and the Bootstrap to Perform Hypothesis Tests on a Population Mean (eText)
    • 10.4 Putting It Together: Which Method Do I Use?
    • 10.5 Hypothesis Tests for a Population Standard Deviation (eText)
    • Chapter 10 Review
    • Chapter Test
    • Making an Informed Decision: Selecting a Mutual Fund
  • 11. Inference on Two Population Parameters
    • 11.1 Inference about Two Population Proportions
    • 11.1A Using Randomization Techniques to Compare Two Proportions (eText)
    • 11.2 Inference about Two Means: Dependent Samples
    • 11.2A Using Bootstrapping to Conduct Inference on Two Dependent Means (eText)
    • 11.3 Inference about Two Means: Independent Samples 11.3A Using Randomization Techniques to Compare Two Independent Means (eText)
    • 11.4 Putting It Together: Which Method Do I Use?
    • 11.5 Inference about Two Population Standard Deviations (eText)
    • Chapter 11 Review
    • Chapter Test
    • Making an Informed Decision: Which Car Should I Buy?
  • 12. Additional Inferential Methods
    • 12.1 Goodness-of-Fit Test
    • 12.2 Tests for Independence and the Homogeneity of Proportions
    • 12.3 Testing the Significance of the Least-Squares Regression Model
    • 12.3A Using Randomization Techniques on the Slope of the Least-Squares Regression Line (eText)
    • 12.4 Confidence and Prediction Intervals
    • Chapter 12 Review
    • Chapter Test
    • Making an Informed Decision: Benefits of College Photo Credits

    Appendix A: Tables

    Appendix B (eText): B.1 Lines; B.2 Inference about Two Population Proportions: Dependent Samples; B.3 Comparing Three or More Means (One-Way Analysis of Variance)


    Subject Index

    Tables and Formulas

About our author

With training in mathematics, statistics and economics, Mike Sullivan, III has a varied teaching background that includes 15 years of instruction in both high school- and college-level mathematics. He is currently a full-time professor of mathematics and statistics at Joliet Junior College. Mike has numerous textbooks in publication in addition to his Introductory Statistics series, including a Developmental Math series and a Precalculus series, which he writes with his father Michael Sullivan.  

Mike has built this book in the classroom using feedback from his students. He is well aware of the challenges of students taking an introductory statistics course. His goal is for students to be more informed interpreters of data, so that they will be better decision makers and have stronger critical-thinking skills. When not in the classroom or writing, Michael enjoys spending time with his wife and 3 children, Michael, Kevin and Marissa, and playing golf. 

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