Seeing Through Statistics,
4th Edition

PART 1: FINDING DATA IN LIFE.
1. THE BENEFITS AND RISKS OF USING STATISTICS
1.1 Statistics. Case study. 1.1 Heart or Hypothalamus? 1.2 Detecting Patterns and Relationships. Case study. 1.2 Does Aspirin Prevent Heart Attacks? 1.3 Don’t Be Deceived by Improper Use of Statistics. CASE STUDY 1.3 [To come]. 1.4 Summary and Conclusions. Thinking About Key Concepts. Exercises. Mini-Projects. References.
2. READING THE NEWS.
2.1 The Educated Consumer of Data. 2.2 Origins of News Stories. 2.3 How to be a Statistics Sleuth: Seven Critical Components. 2.4 Four Hypothetical Examples of Bad Reports. CASE STUDY 2.1 Who Suffers from Hangovers? 2.5 Planning Your Own Study: Defining the Components in Advance. CASE STUDY 2.2. Thinking About Key Concepts. Exercises. Mini-Projects. References.
3. MEASUREMENTS, MISTAKES, AND MISUNDERSTANDINGS.
3.1 Simple Measures Don’t Exist. 3.2 It’s All in the Wording. CASE STUDY 3.1 No Opinion of Your Own? Let Politics Decide. 3.3 Open or Closed Questions: Should Choices Be Given?. 3.4 Defining What Is Being Measured. CASE STUDY 3.2. 3.5 Defining a Common Language. Thinking About Key Concepts. Exercises. Mini-Projects. References.
4. HOW TO GET A GOOD SAMPLE.
4.1 Common Research Strategies. 4.2 Defining a Common Language. 4.3 The Beauty of Sampling. 4.4 Simple Random Sampling. 4.5 Other Sampling Methods. 4.6 Difficulties and Disasters in Sampling. CASE STUDY 4.1 The Infamous Literary Digest Poll of 1936. Thinking About Key Concepts. Exercises. Mini-Projects. References.
5. EXPERIMENTS AND OBSERVATIONAL STUDIES.
5.1 Defining a Common Language. 5.2 Designing a Good Experiment. CASE STUDY 5.1 Quitting Smoking with Nicotine Patches. 5.3 Difficulties and Disasters in Experiments. CASE STUDY 5.2 Beat the Heat with a Frozen Treat. 5.4 Designing a Good Observational Experiment. CASE STUDY 5.3. 5.5 Difficulties and Disasters in Observational Studies. 5.6 Random Sample versus Random Assignment. Thinking About Key Concepts. Exercises. Mini-Projects. References
6. GETTING THE BIG PICTURE.
6.1 Final Questions. CASE STUDY 6.1 Mozart, Relaxation, and Performance on Spatial Tasks. CASE STUDY. 6.2 Can Meditation Boost Test Scores?. CASE STUDY 6.3 Drinking, Driving, and the Supreme Court. CASE STUDY 6.4 Smoking During Pregnancy and Child’s IQ. CASE STUDY 6.5 For Class Discussion: Guns and. Homicides at Home. Mini-Projects. References.
PART II: FINDING LIFE IN DATA.
7. SUMMARIZING AND DISPLAYING MEASUREMENT DATA.
7.1 Turning Data into Information. 7.2 Picturing Data: Stemplots and Histograms. 7.3 Five Useful Numbers: A Summary. 7.4 Boxplots. 7.5 Traditional Measures: Mean, Variance, and Standard Deviation. 7.6 Caution: Being Average Isn’t Normal. CASE STUDY 7.1 Detecting Exam Cheating with a Histogram. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
8. BELL-SHAPED CURVES AND OTHER SHAPES.
8.1 Populations, Frequency Curves, and Proportions. 8.2 The Pervasiveness of Normal Curves. 8.3 Percentiles and Standardized Scores. 8.4 z-Scores and Familiar Intervals. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. References
9. PLOTS, GRAPHS, AND PICTURES.
9.1 Well-Designed Statistical Pictures. 9.2 Pictures of Categorical Data. 9.3 Pictures of Measurement Variables. 9.4 Pictures Trends across Time. 9.5 Difficulties and Disasters in Plots, Graphs, and Pictures.
9.6 A Checklist for Statistical Pictures. CASE STUDY 9.1. Thinking About Key Concepts. Exercises. Mini-Projects. References.
10. RELATIONSHIPS BETWEEN MEASUREMENT VARIABLES.
10.1 Statistical Relationships. 10.2 Strength versus Statistical Significance. 10.3 Measuring Strength Through Correlation. 10.4 Specifying Linear Relationships with Regression. CASE STUDY 10.1 Are Attitudes about Love and Romance Hereditary? CASE STUDY 10.2 A Weighty Issue: Women Want Less, Men Want More. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
11. RELATIONSHIPS CAN BE DECEIVING.
11.1 Illegitimate Correlations. 11.2 Legitimate Correlation Does Not Imply Causation. 11.3 Some Reasons for Relationships Between Variables. 11.4 Confirming Causation. Case Study 11.1. Thinking About Key Concepts. Exercises. Mini-Projects. References.
12. RELATIONSHIPS BETWEEN CATEGORICAL VARIABLES
12.1 Displaying Relationships Between Categorical Variables: Contingency Tables. 12.2 Relative Risk, Increased Risk, and Odds. 12.3 Misleading Statistics about Risk. 12.4 Simpson’s Paradox: The Missing Third Variable. CASE STUDY 12.1 Assessing Discrimination in Hiring and Firing. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
CH 13. STATISTICAL SIGNIFICANCE FOR 2X2 TABLES
13.1 Measuring the Strength of the Relationship. 13.2 Steps for Assessing Statistical Significance. 13.3 The Chi-Square Test. 13.4 Practical versus Statistical Significance. CASE STUDY 13.1 Extrasensory Perception Works Best with Movies. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
PART III: UNDERSTANDING UNCERTAINTY IN LIFE.
14. UNDERSTANDING PROBABILITY AND LONG-TERM EXPECTATIONS
14.1 Probability. 14.2 The Relative-Frequency Interpretation. 14.3 The Personal-Probability Interpretation. 14.4 Applying Some Simple Probability Rules. 14.5 When Will It Happen?
14.6 Long-Term Gains, Losses, and Expectations. CASE STUDY 14.1 Birthdays and Death Days—Is There a Connection? Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
15. UNDERSTANDING UNCERTAINTY THROUGH SIMULATION
15.1 Mimicking Reality through Simulation. 15.2 Simulating the Chi-Square Test. 15.3 Randomization Tests. 15.4 Simulating Probabilities. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
16. PSYCHOLOGICAL INFLUENCES ON PERSONAL PROBABILITY
16.1 Revisiting Personal Probability. 16.2 Equivalent Probabilities; Different Decisions. 16.3 How Personal Probabilities can Be Distorted. 16.4 Optimism, Reluctance to Change, and Overconfidence. 16.5 Calibrating Personal Probabilities of Experts. CASE STUDY 16.1 Calibrating Weather Forecasters and Physicians. 16.6 Tips for Improving Your Personal Probabilities and Judgments. Thinking About Key. Concepts. Exercises. Mini-Projects. References.
17. WHEN INTUITION DIFFERS FROM RELATIVE FREQUENCY
17.1 Revisiting Relative Frequency. 17.2 Coincidences. 17.3 The Gambler’s Fallacy. 17.4 Confusion of the Inverse. CASE STUDY 17.1 Streak Shooting in Basketball: Reality or Illusion?. 17.5 Using Expected Values to Make Wise Decisions. CASE STUDY 17.2. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
18. UNDERSTANDING UNCERTAINTY IN ECONOMICS.
18.1 Cost of Living: The Consumer Price Index. 18.2 Uses of the Consumer Price Index. 18.3 Criticisms of the Consumer Price Index. 18.4 Seasonal Adjustments: Reporting the Consumer Price Index 18.5 Economic Indicators. CASE STUDY 18.1 Did Wages Really Go Up in the Reagan–Bush Years? Thinking About Key Concepts. Exercises. Mini-Projects. References.
PART IV: MAKING JUDGMENTS FROM SURVEYS AND EXPERIMENTS.
19. THE DIVERSITY OF SAMPLES FROM THE SAME POPULATION.
19.1 Setting the Stage. 19.2 What to Expect of Sample Proportions. 19.3 What to Expect of Sample Means. 19.4 What to Expect in Other Situations. CASE STUDY 19.1. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
20. ESTIMATING PROPORTIONS WITH CONFIDENCE.
20.1 Confidence Intervals. 20.2 Three Examples of Confidence Intervals from the Media. 20.3 Constructing a Confidence Interval for a Proportion. CASE STUDY 20.1. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
CH 21. THE ROLE OF CONFIDENCE INTERVALS IN RESEARCH.
21.1 Confidence Intervals for Population Means. 21.2 Confidence Intervals for the Difference Between Two Means. 21.3 Revisiting Case Studies: How Journals Present Confidence Intervals. 21.4 Understanding Any Confidence Interval. CASE STUDY 21.1 Premenstrual Syndrome? Try Calcium. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
22. REJECTING CHANCE—TESTING HYPOTHESES IN RESEARCH.
22.1 Using Data to Make Decisions. 22.2 The Basic Steps for Testing Hypotheses. 22.3 Testing Hypotheses for Proportions. 22.4 What Can Go Wrong: The Two Types of Errors. CASE STUDY 22.1 Testing for the Existence of Extrasensory Perception. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
23. HYPOTHESIS TESTING—EXAMPLES AND CASE STUDIES.
23.1 How Hypothesis Tests are Reported in the News. 23.2 Testing Hypotheses about Proportions and Means. 23. 3 Revisiting Case Studies: How Journals Present Hypothesis Tests. CASE STUDY 23.1 An Interpretation of a p-Value Not Fit to Print. Thinking About Key Concepts. For Those Who Like Formulas. Exercises. Mini-Projects. References.
24. SIGNIFICANCE, IMPORTANCE, AND UNDETECTED DIFFERENCES.
24.1 Real Importance versus Statistical Significance. 24.2 The Role of Sample Size in Statistical Significance. 24.3 No Difference versus No Statistically Significant Difference. CASE STUDY 24.1 Does Eating Breakfast Cereal Produce More Boys? 24.4 Multiple Tests and False Positives. 24.5 A Summary of Warnings. CASE STUDY 24.2. Thinking About Key Concepts. Exercises. Mini-Projects. References.
25. META-ANALYSIS: RESOLVING INCONSISTENCIES ACROSS STUDIES.
25.1 The Need for Meta-Analysis. 25.2 Two Important Decisions for the Analyst. CASE STUDY 25.1 Smoking and Reduced Fertility. 25.3 Some Benefits of Meta-Analysis. 25.4 Criticisms of Meta-Analysis. CASE STUDY 25.2 Controversy over Breast Cancer Screening. Thinking About Key Concepts. Exercises. Mini-Projects. References.
26. ETHICS IN STATISTICAL STUDIES.
26.1 Ethical Treatment of Human and Animal Participants. 26.2 Assurance of Data Quality. 26.3 Appropriate Statistical Analyses. 26.4 Fair Reporting of Results. CASE STUDY 26.1 Science Fair Project or Fair Science Project? Exercises. References.
27. PUTTING WHAT YOU HAVE LEARNED TO THE TEST.

• Jessica M. Utts

  Jessica Utts is professor emerita of statistics at the University of California at Irvine. She received her B.A. in math and psychology at SUNY Binghamton and her M.A. and Ph.D. in statistics at Penn State University. Aside from MIND ON STATISTICS, Dr. Utts has authored SEEING THROUGH STATISTICS and co-authored STATISTICAL IDEAS AND METHODS, both published by Cengage Learning. Dr. Utts has been active in the statistics education community at the high school and college levels. She served as chair of the Advanced Placement Statistics Development Committee and as Chief Reader for the A.P. Statistics Exam. In 2016 she was the president of the American Statistical Association, the largest professional organization in the world for statisticians. She is a recipient of the Academic Senate Distinguished Teaching Award and the Magnar Ronning Award for Teaching Excellence, both at the University of California at Davis. She is also a fellow of the American Statistical Association, the Institute of Mathematical Statistics, the American Association for the Advancement of Science and the Association for Psychological Science. Beyond statistics education, Dr. Utts’s major contributions have been in applying statistics to a variety of disciplines, most notably to parapsychology, the laboratory study of psychic phenomena. She has appeared on numerous television shows, including Larry King Live, ABC's Nightline, CNN Morning News and 20/20.

• InfoTrac® Student Collections are specialized databases expertly drawn from the Gale Academic One library. Each InfoTrac® Student Collection enhances the student learning experience in the specific course area related to the product. These specialized databases allow access to hundreds of scholarly and popular publications - all reliable sources - including journals, encyclopedias, and academic reports. Learn more and access at: http://gocengage.com/infotrac.

• The book includes many updated examples and new case studies.

• The chapter on Time Series has been split up and moved into the chapter on graphs and the chapter on Economic News.

• The chapter previously called Reading the Economic News has been changed to “Understanding Uncertainty in Economics” and moved to the end of Part 3.

• NEW for Fall 2020 - Turn your students into statistical thinkers with the Statistical Analysis and Learning Tool (SALT). SALT is an easy-to-use data analysis tool created with the intro-level student in mind. It contains dynamic graphics and allows students to manipulate data sets in order to visualize statistics and gain a deeper conceptual understanding about the meaning behind data. SALT is built by Cengage, comes integrated in Cengage WebAssign Statistics courses and available to use standalone.

• There is a new Section at the end of each chapter called “Thinking about Key Concepts” that “closes the loop” on the Thought Questions presented at the beginning of that chapter.

• A “Guide for Educated Citizens” theme is woven throughout the book to increase student's interest in text. This theme underscores the importance of statistical literacy to everyday life. This includes understanding the difference between statistical significance and practical importance, the idea that coincidences and improbable events are not collectively uncommon, and other frequently misunderstood statistical concepts.

• Java applets have been included on the companion website that accompanies the text. The applets give students more opportunity for hands-on learning and allow students to explore statistics on their own.

• Case studies will be on the companion website and woven throughout the text in examples and exercises. Each case study includes one or more news stories, and the journal article or report from which the stories were derived. This allows students to understand where statistical news stories originate.

• Excel commands for finding probabilities for normal curves are incorporated.

• Dozens of case studies and numerous examples are based on real data, real news stories, research summaries, and examples of misuses of statistics.

• Most chapters begin with “Thought Questions” to facilitate discussion of the chapter's primary ideas, and most include several mini-projects that provide plenty of practice for students.

• “For Those Who like Formulas” features appear at the end of appropriate chapters. This boxed feature is optional, and contains all the mathematical notation and formulas pertinent for the chapter material.

• The book's chapters are designed to allow one lecture for each chapter, or two chapters per week. The text's organization makes it very easy for instructors to design their syllabus around the text.

• Case studies will be on the companion website and woven throughout the text in examples and exercises. Each case study includes one or more news stories, and the journal article or report from which the stories were derived. This allows students to understand where statistical news stories originate.

• Examples, exercises, and case studies have been updated.

• Most chapters begin with “Thought Questions” to facilitate discussion of the chapter's primary ideas, and most include several mini-projects that provide plenty of practice for students.

• Dozens of case studies and numerous examples are based on real data, real news stories, research summaries, and examples of misuses of statistics.

Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

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