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| Foreword by George Cobb | |
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| Preface | |
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| To the Instructor | |
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| To the Student | |
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| Acknowledgments | |
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| Introduction: Why Data Matters | |
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| Statistics in the News | |
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| Basic Concepts of Statistical Thinking Presented in the Context of Categorical Data | |
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| Proportions in Samples, Proportions in Populations | |
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| The Most Popular News Statistics | |
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| Percentage, Proportions, Raw Counts, Pie Charts, Bar Charts | |
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| How Many People Are There? | |
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| U.S. and World Populations, Population Growth, Proportional Changes, The Unemployment Rate, X-Y Plots | |
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| Things Vary, and Small Samples Vary the Most | |
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| The Law of Large Numbers | |
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| The Pattern in Random Sample Proportions | |
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| Taking a Good Sample of a Population | |
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| Representative and Biased Samples, Random Sampling, Self-Selected Samples | |
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| How Samples Vary | |
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| Histograms, Bell Curves | |
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| How Widely Samples Vary | |
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| The Standard Error of a Proportion, The Normal Distribution | |
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| Making Inferences | |
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| Forecasting the Future | |
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| Prediction Intervals for Sample Proportions | |
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| What a Sample Reveals About a Population | |
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| Confidence Intervals for Proportions in Populations | |
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| The Story of Statistical Inference | |
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| Null Hypothesis, P-Value, Alpha, Significance | |
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| Testing Locations and Differences of Proportions | |
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| Testing Where a Proportion Is | |
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| The Z-Test | |
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| How to Look for Differences in Chances | |
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| Cross-Tabulations, Correlation, The Null Hypothesis of the Chi-Square Test | |
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| Checking for No Correlation with the Chi-Square Test | |
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| Chi-Square Distribution, Degrees of Freedom, Correlation Is Not Causation | |
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| Averages and Other Number Line Statistics in the News | |
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| Incomes and Other Quantities | |
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| Medians, Number Line Observations,Means | |
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| Which Tells the Truth-The Mean, the Median . . . or 281 the Weighted Mean? | |
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| Standard Deviation, Spread,Weighted Means | |
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| Inflation and the Consumer Price Index | |
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| Adjusting for Inflation | |
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| Statistics in Science | |
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| Descriptive and Inferential Statistics for Continuous Data | |
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| What Sample Data Distribution Reveals About the Population | |
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| Exploratory Data Analysis | |
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| Histograms, Stem-and-Leaf Plots, Box Plots | |
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| Describing Number Line Variation | |
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| Standard Deviation of Samples,Variance | |
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| How to See the Future | |
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| Prediction Intervals for Number Line Observations and Sample Means, Standard Error of Means, Confidence | |
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| Intervals for Population Means, Central Limit Theorem | |
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| Testing Treatments | |
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| A Cautionary Tale | |
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| William Gosset's Troubles with the Z-Test and the T-Distribution, The T-Test | |
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| How to Test Whether a Treatment Works | |
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| The Logic of Experiments, Correlational Studies | |
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| Variances Between and Within | |
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| Estimating the Population Variance from Variation Within Groups and from Variation Between Group Means | |
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| Analysis of Variance | |
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| Fisher's Analysis of Variance | |
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| Calculating Fisher's F-Value | |
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| What If the Data Are Not Normally Distributed? | |
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| The Effect of Nonconstant Variances and Non-normality on ANOVA, Nonparametric Tests | |
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| American Counties | |
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| Correlation, Scatter Plots | |
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| Best Lines | |
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| Lines | |
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| How to Calculate the Equation of a Line | |
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| Finding Best-Fitting Lines | |
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| The Least Squares Line | |
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| An Excellent Line | |
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| Calculating the Slope of the Regression Equation, Standard Error and Confidence Interval for the Regression Equation | |
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| Tests of Regression | |
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| How to Test the Regression Models | |
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| R2, Pearson's r, A T-Test for the Regression Slope | |
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| What | |