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Introductory Statistics: Exploring the World Through Data, 4th edition

Published by Pearson (June 11, 2024) © 2025

  • Robert N. Gould University of California, Los Angeles
  • Rebecca Wong West Valley College
  • Colleen Ryan California Lutheran University

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For courses in Introductory Statistics.

Inspire students to examine data and make discoveries

Introductory Statistics: Exploring the World Through Data helps students learn to think critically with and about data, communicate their findings to others, and evaluate others’ arguments. Crafted by authors who are active in the classroom and in the statistics education community, it pairs a clear, conversational writing style with new and frequent opportunities to apply statistical thinking.

The 4th Edition fully revises all end-of-chapter Data Projects, updates technology guides to match current hardware and software, adds hundreds of new exercises and updates previous exercises, and much more.

Hallmark features of this title

  • The Data Cycle approach guides students through the statistical investigation process, using the phases Ask Questions, Consider Data, Analyze Data, and Interpret Data. A margin icon notes when the Data Cycle is particularly relevant. 
  • Large data sets throughout focus on different variables, illustrating how data “moves” depending on the concept or question explored.
  • Snapshots break down the statistical concepts introduced, quickly summarizing the concept or procedure and indicating when and how it should be used.
  • Guided Exercises step students through solving a problem if they need extra help while doing homework.
  • 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. 
  • The Data Moves icon points students to the "raw" or original data from which the examples were taken, to help students understand how data must be wrangled in order to be made suitable for analysis.

New and updated features of this title

  • Increased emphasis on formulating “statistical investigative questions” as an important first step in the Data Cycle gives students more practice in formulating questions that will help them interpret data.
    • To formulate questions is to engage in mathematical and statistical modeling; the 4th Edition spends more time teaching this important skill. 
  • Thoroughly revised supplemental Data Projects that introduce students to the field of data science are now more compact and streamlined. Students completing the Data Projects will develop basic skills in data analysis that will enable them to analyze other statistical questions of interest. 
    • Chapter 1 introduces a messy data set from the open data portal of the City of Los Angeles concerning rentals of e-bikes. In each subsequent chapter, students are introduced to skills needed to clean the data and engage in investigative statistical questions and reasoning.
  • Updated technology guides match current hardware and software. 
  • Hundreds of new exercises and updates of previous exercises, new and updated examples in each chapter, and new and updated data sets with more large data are provided.

Features of MyLab Statistics for the 4th Edition

  • Exercises with immediate feedback reflect the approach and learning style of the text and regenerate algorithmically to provide unlimited opportunity for practice and mastery. Most include learning aids such as guided solutions and sample problems, and offer helpful feedback when students enter incorrect answers. Approx. 200 exercises are updated for new data and clarity.
  • Personal Inventory Assessments are a collection of online exercises designed to promote self-reflection and metacognition in students. These 33 assessments include topics such as a Stress Management Assessment, Diagnosing Poor Performance and Enhancing Motivation, and Time Management Assessment. 
  • Dynamic Study Modules work by continuously assessing student performance and activity, then using data and analytics to provide personalized content in real-time to reinforce concepts targeting each student’s strengths and weaknesses.
  • Data Cycle Videos walk students through a four-phase framework for problem solving: Ask Questions, Consider Data, Analyze Data, and Interpret Data.
  • Data Projects are powered by MediaShare; instructors can assign projects and use editable rubrics to grade student document and video submissions.
  • New Section Lecture Videos are available to assign with pause-and-predict questions that offer students the opportunity to practice as they learn. Pause-and-Predict Video Questions also offer students practice opportunities as they learn.

Index of Applications

1. Introduction to Data

  • Case Study: Dangerous Habit?
  • 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
  • Data Project: Introduction: Importing Data

2. Picturing Variation with Graphs

  • Case Study: Student-to-Teacher Ratio at Colleges
  • 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
  • Data Project: The Bare Bones: Data Types

3. Numerical Summaries of Center and Variation

  • Case Study: Living in a Risky World
  • 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
  • Data Project: The W’s and One H: Asking Interrogative Questions

4. Regression Analysis: Exploring Associations between Variables

  • Case Study: Forecasting Home Prices
  • 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
  • Data Project: Get It Sorted: Sorting Data

5. Modeling Variation with Probability

  • Case Study: SIDS or Murder?
  • 5.1 What Is Randomness?
  • 5.2 Finding Theoretical Probabilities
  • 5.3 Associations in Categorical Variables
  • 5.4 Finding Empirical Probabilities
  • Data Project: Ask Good Questions: Posing Statistical Investigative Questions

6. Modeling Random Events: The Normal and Binomial Models

  • Case Study: You Sometimes Get More Than You Pay for
  • 6.1 Probability Distributions Are Models of Random Experiments
  • 6.2 The Normal Model
  • 6.3 The Binomial Model (Optional)
  • Data Project: Transformations for a Better Analysis

7. Survey Sampling and Inference

  • Case Study: Spring Break Fever: Just What the Doctors Ordered?
  • 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
  • Data Project: Make it Smaller: Subsetting Data

8. Hypothesis Testing for Population Proportions

  • Case Study: Dodging the Question
  • 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
  • Data Project: On and Off, and Off and On: Working with Binary Variables

9. Inferring Population Means

  • Case Study: You Look Sick! Are You Sick?
  • 9.1 Sample Means of Random 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
  • Data Project: Non-binary Coding: Creating Multi-valued Categorical Variables

10. Associations between Categorical Variables

  • Case Study: Popping Better Popcorn
  • 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
  • Data Project: Dating Yourself: Creating New Variables from Dates

11. Multiple Comparisons and Analysis of Variance

  • Case Study: Seeing Red
  • 11.1 Multiple Comparisons
  • 11.2 The Analysis of Variance
  • 11.3 The ANOVA Test
  • 11.4 Post-Hoc Procedures
  • Data Project: What’s in a Name: Splitting and Merging Columns

12. Experimental Design: Controlling Variation

  • Case Study: Does Stretching Improve Athletic Performance?
  • 12.1 Variation Out of Control
  • 12.2 Controlling Variation in Surveys
  • 12.3 Reading Research Papers
  • Data Project: Making Stacks: Stacking and Unstacking Data

13. Inference without Normality

  • Case Study: Contagious Yawns
  • 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
  • Data Project: Breathe Deeply: Prepare a Data Set for Analysis

14. Inference for Regression

  • Case Study: Another Reason to Stand at Your Desk?
  • 14.1 The Linear Regression Model
  • 14.2 Using the Linear Model
  • 14.3 Predicting Values and Estimating Means
  • Data Project: Is the Pen Mightier than the Keyboard?

Appendices

  • A: Tables
  • B: Answers to Odd-Numbered Exercises
  • C: Credits

Index

About our authors

Robert L. Gould (Ph.D., University of California, San Diego) is a leader in the statistics education community. He has served in leadership roles in several national committees, including the Joint Committee of American Mathematical Association of Two-Year Colleges and the American Statistical Association. He was a co-author of the 2005 edition of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report as well as the 2020 GAISE preK-12 Report. In 2011 he founded DataFest, an undergraduate competition and celebration of data that is hosted by the American Statistical Association. As lead principal investigator of the NSF-funded Mobilize Project, he led the development of the first high school-level data science course, which is taught in schools throughout the U.S.

Rob teaches in the Department of Statistics and Data Science at UCLA. In 2012, he was elected Fellow of the American Statistical Association, and in 2019 was awarded the American Statistical Association Waller Distinguished Teaching Career Award and the United States Conference on Teaching Statistics Lifetime Achievement Award. In his free time, Rob plays the cello and enjoys attending classical music concerts.

Rebecca K. Wong has taught mathematics and statistics at West Valley College for more than 20 years. She enjoys designing activities to help students explore statistical concepts and encouraging students to apply those concepts to areas of personal interest.

Rebecca earned at 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.

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 Moorpark Community College. 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, Colleen sings, has been an avid skier, and enjoys time with her family.

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