Introductory Statistics: Exploring the World Through Data, 4th Edition © 2025

Introductory Statistics: Exploring the World Through Data, published by Pearson, shows students how to apply statistical thinking. Real-world data in the text build skills in analysis and interpretation while the conversational tone turns a seemingly difficult subject into an engaging experience.

  • Data-centric approach encourages frequent practice
  • Built-in guidance helps connect statistics to students’ lives
  • Emphasis on using statistical software keeps the focus on concepts

Please note: the 4th edition digital course will not be ready until late September 2024. Users will be able to access the 3rd edition prior to this date, and migrate to the 4th edition when it is ready.


Introductory Statistics Resources

Introductory Statistics uses active learning and current data to teach the exploration and analysis of real information.


Learn by Doing

Rather than make students memorize facts and figures, Introductory Statistics involves the class in solving problems. That way, they will carry what they learned with them past the last day of class.


Approachable Style

The statistics courseware uses simple and conversational language intelligently so complex concepts can be quickly delivered and understood then applied.


Contemporary Data

Examples and exercises are built off of data from news stories and everyday life that connect to students in ways that create interest and engagement.


Technological Integration

This program leverages online resources that can be used in-class and at home. Teachers have the option of blended instruction or 100% digital solutions.

Introductory Statistics Teaching Solutions

  • Data-Centric Approach
  • Built-in Guidance
  • Technology Integration
  • Learner Empowerment through Trusted Content
  • Data Moves
    New Data Moves directs students to the original version of a dataset that was modified for the textbook and explains how parts were extracted to answer a statistical question. This prepares students to do their own data manipulation in the end-of-chapter Data Projects.
  • Data Projects
    New End-of-Chapter Data Projects emphasize critical thinking and data analysis skills. They ask students to move through the entire data cycle in order to make a data-informed decision and communicate their findings. Data Projects are assignable through MyMathLab for School and assume the use of StatCrunch or another statistical software package.
  • Data Cycle
    New Introduction to the Data Cycle, an author-created framework, guides students through data exploration as an investigative process. It proceeds from data collection to data analysis.
  • Data Sets
    Large data sets reappear throughout the book to focus on different variables. This illustrates how data “moves” depending on the concept or question being explored.
  • Case Studies
    Updated Case Studies open each chapter, showing a real-world application of the concepts. At the end of the chapter, the case study is revisited to show how the statistical techniques covered in the chapter help to solve the problem presented.
  • Snapshots
    Snapshots go beyond a definition by breaking down the statistical concepts introduced in the text discussion. A quick summary of the concept or procedure indicates when and how it should be used.
  • Guided Exercises
    Guided Exercises step students through solving a problem if they need extra help while doing homework. These exercises are marked with an icon to indicate that step-by-step instruction is available in the Guided Exercises section at the end of each chapter.
  • Mathematical Formulas
    All procedures and concepts in the text assume that students have access to some technology or statistical software package. This approach introduces mathematical formulas only when necessary to understand the concept.
  • TechTips
    TechTips outline steps for performing calculations using TI-83/84-Plus® graphing calculators, Excel®, Minitab®, and StatCrunch®. Whenever a new method or procedure is introduced, an icon refers students to the TechTips section at the end of the chapter.
  • Assessment Questions
    New assessment questions are tied to Data Cycle videos that demonstrate how data collection and analysis can be applied to answer questions about everyday life.
  • Applet Modules
    New Interactive Applet Modules help students visualize statistical concepts and apply them to real-world situations. Modules introduce students to a concept, walk them through an example, and close by asking them to answer a series of application questions. Interactive Applet Modules are assignable along with 13 existing standalone StatCrunch applets.
  • Conceptual Question Library
    Updated question types are easy to add to assignments and provide more opportunities to practice statistical thinking and streamlining organization. The Conceptual Question Library (CQL) is now correlated by chapter, making it easier to include it in assignments.
  • Chapter Review Videos
    Updated Chapter Review videos by co-author Rebecca Wong and Carrie Grant (Flagler College) walk students through key examples in the text.

Empower Your Math Students with the MyMathLab® Platform from Pearson


MyMathLab® for School from Pearson empowers teachers to help every student at every level of math succeed with differentiated online resources.

More About Introductory Statistics

  • Robert L. Gould Author Bio
    Robert L. Gould (Ph.D., University of California–San Diego) is a leader in the statistics education community. He has served as chair of the AMATYC/ASA joint committee, was co-leader of the Two-Year College Data Science Summit hosted by the American Statistical Association, served as chair of the ASA’s Statistics Education Section, and was a co-author of the 2005 Guidelines for Assessment in Instruction on Statistics Education (GAISE) College Report. While serving as the Associate Director of Professional Development for CAUSE (Consortium for the Advancement of Undergraduate Statistics Education), he worked closely with the American Mathematical Association of Two-Year Colleges (AMATYC) to provide traveling workshops and summer institutes in statistics. He was the lead principal investigator of the NSF-funded Mobilize Project, which developed and implemented the first high-school level data science course. For over twenty years, he has served as Vice-Chair of Undergraduate Studies at the UCLA Department of Statistics, and is Director of the UCLA Center for the Teaching of Statistics. In 2012, Rob was elected Fellow of the American Statistical Association.
  • Colleen N. Ryan Author Bio
    Colleen N. Ryan has taught statistics, chemistry, and physics to diverse community college students for decades. She taught at Oxnard College from 1975 to 2006, where she earned the Teacher of the Year Award. Colleen currently teaches statistics part-time at California Lutheran University. She often designs her own lab activities. Her passion is to discover new ways to make statistical theory practical, easy to understand, and sometimes even fun. Colleen earned a B.A. in physics from Wellesley College, an M.A.T. in physics from Harvard University, and an M.A. in chemistry from Wellesley College. Her first exposure to statistics was with Frederick Mosteller at Harvard. In her spare time, she sings with the Oaks Chamber Singers and enjoys time with her family.
  • Rebecca K. Wong Author Bio
    Rebecca K. Wong has taught mathematics and statistics at West Valley College for more than twenty years. She enjoys designing activities to help students actively explore statistical concepts and encouraging students to apply those concepts to areas of personal interest. Rebecca earned a B.A. in mathematics and psychology from the University of California–Santa Barbara, an M.S.T. in mathematics from Santa Clara University, and an Ed.D. in Educational Leadership from San Francisco State University. She has been recognized for outstanding teaching by the National Institute of Staff and Organizational Development and the California Mathematics Council of Community Colleges. When not teaching, Rebecca is an avid reader and enjoys hiking trails with friends.
  • Table of Contents

    1: Introduction to Data

    • 1.1 What Are Data?
    • 1.2 Classifying and Storing Data
    • 1.3 Investigating Data
    • 1.4 Organizing Categorical Data
    • 1.5 Collecting Data to Understand Causality

    2: Picturing Variation with Graphs

    • 2.1 Visualizing Variation in Numerical Data
    • 2.2 Summarizing Important Features of a Numerical Distribution
    • 2.3 Visualizing Variation in Categorical Variables
    • 2.4 Summarizing Categorical Distributions
    • 2.5 Interpreting Graphs

    3: Numerical Summaries of Center and Variation

    • 3.1 Summaries for Symmetric Distributions
    • 3.2 What's Unusual? The Empirical Rule and z-Scores
    • 3.3 Summaries for Skewed Distributions
    • 3.4 Comparing Measures of Center
    • 3.5 Using Boxplots for Displaying Summaries

    4: Regression Analysis: Exploring Associations between Variables

    • 4.1 Visualizing Variability with a Scatterplot
    • 4.2 Measuring Strength of Association with Correlation
    • 4.3 Modeling Linear Trends
    • 4.4 Evaluating the Linear Model

    5: Modeling Variation with Probability

    • 5.1 What Is Randomness?
    • 5.2 Finding Theoretical Probabilities
    • 5.3 Associations in Categorical Variables
    • 5.4 Finding Empirical Probabilities

    6: Modeling Rando Events: The Normal and Binomial Models

    • 6.1 Probability Distributions Are Models of Random Experiments
    • 6.2 The Normal Model
    • 6.3 The Binomial Model (Optional)

    7: Survey Sampling and Inference

    • 7.1 Learning about the World through Surveys
    • 7.2 Measuring the Quality of a Survey
    • 7.3 The Central Limit Theorem for Sample Proportions
    • 7.4 Estimating the Population Proportion with Confidence Intervals
    • 7.5 Comparing Two Population Proportions with Confidence

    8: Hypothesis Testing for Population Proportions

    • 8.1 The Essential Ingredients of Hypothesis Testing
    • 8.2 Hypothesis Testing in Four Steps
    • 8.3 Hypothesis Tests in Detail
    • 8.4 Comparing Proportions from Two Populations

    9: Inferring Population Means

    • 9.1 Sample Means of Rando Samples
    • 9.2 The Central Limit Theorem for Sample Means
    • 9.3 Answering Questions about the Mean of a Population
    • 9.4 Hypothesis Testing for Means
    • 9.5 Comparing Two Population Means
    • 9.6 Overview of Analyzing Means

    10: Associations between Categorical Variables

    • 10.1 The Basic Ingredients for Testing with Categorical Variables
    • 10.2 The Chi-Square Test for Goodness of Fit
    • 10.3 Chi-Square Tests for Associations between Categorical Variables
    • 10.4 Hypothesis Tests When Sample Sizes Are Small

    11: Multiple Comparisons and Analysis of Variance

    • 11.1 Multiple Comparisons
    • 11.2 The Analysis of Variance
    • 11.3 The ANOVA Test
    • 11.4 Post-Hoc Procedures

    12: Experimental Design: Controlling Variation

    • 12.1 Variation Out of Control
    • 12.2 Controlling Variation in Surveys
    • 12.3 Reading Research Papers

    13: Inference without Normality

    • 13.1 Transforming Data
    • 13.2 The Sign Test for Paired Data
    • 13.3 Mann-Whitney Test for Two Independent Groups
    • 13.4 Randomization Tests

    14: Inference for Regression

    • 14.1 The Linear Regression Model
    • 14.2 Using the Linear Model
    • 14.3 Predicting Values and Estimating Means

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