Introduction to Statistical Investigations, Second Edition provides a unified framework for explaining variation across study designs and variable types, helping students increase their statistical literacy and appreciate the indispensable role of statistics in scientific research. Requiring only basic algebra as a prerequisite, the program uses the immersive, simulation-based inference approach for which the author team is known. Students engage with various aspects of data collection and analysis using real data and clear explanations designed to strengthen multivariable understanding and reinforce concepts. Each chapter follows a coherent six-step statistical exploration and investigation method (ask a research question, design a study, explore the data, draw inferences, formulate conclusions, and look back and ahead) enabling students to assess a variety of concepts in a single assignment. Challenging questions based on research articles strengthen critical reading skills, fully worked examples demonstrate essential concepts and methods, and engaging visualizations illustrate key themes of explained variation. The end-of-chapter investigations expose students to various applications of statistics in the real world using real data from popular culture and published research studies in variety of disciplines. Accompanying examples throughout the text, user-friendly applets enable students to conduct the simulations and analyses covered in the book.

Table of Contents:
Preliminaries Introduction to Statistical Investigations 1 Section P.1: Introduction to the Six-Step Method 2 Example P.1: Organ Donations 2 Section P.2: Exploring Data 7 Example P.2: Oh, Say Can You Sing? 7 Section P.3: Exploring Random Processes 14 Exploration P.3: Cars or Goats 14 Unit 1 Four Pillars of Inference: Strength, Size, Breadth, and Cause 30 1 Significance: How Strong Is the Evidence? 31 Section 1.1: Introduction to Chance Models 32 Example 1.1: Can Dolphins Communicate? 33 Exploration 1.1: Can Dogs Understand Human Cues? 41 Section 1.2: Measuring the Strength of Evidence 45 Example 1.2: Rock-Paper-Scissors 46 Exploration 1.2: Tasting Water 52 Section 1.3: Alternative Measure of Strength of Evidence 57 Example 1.3: Heart Transplant Operations 58 Exploration 1.3: Do People Use Facial Prototyping? 62 Section 1.4: What Impacts Strength of Evidence? 66 Example 1.4: Predicting Elections from Faces? 66 Exploration 1.4: Competitive Advantage to Uniform Colors? 72 Section 1.5: Inference for a Single Proportion: Theory-Based Approach 75 Example 1.5: Halloween Treats 77 Exploration 1.5: Eye Dominance 80 2 Generalization: How Broadly Do the Results Apply? 117 Section 2.1: Sampling from a Finite Population: Proportions 118 Example 2.1: Voter Turnout 119 Exploration 2.1: Sampling Words 126 Section 2.2: Quantitative Data 133 Example 2.2: Sampling Students 134 Exploration 2.2: Sampling Words (cont.) 138 Section 2.3: Theory-based Inference for a Population Mean 143 Example 2.3: Estimating Elapsed Time 143 Exploration 2.3: Sleepless Nights? 150 Section 2.4: Other Statistics 154 Example 2.4: Estimating Elapsed Time (cont.) 154 Exploration 2.4: Backpack Weights 160 3 Estimation: How Large Is the Effect? 187 Section 3.1: Statistical Inference: Confidence Intervals 188 Example 3.1: Can Dogs Sniff Out Cancer? 189 Exploration 3.1: Kissing Right? 194 Section 3.2: 2SD and Theory-Based Confidence Intervals for a Single Proportion 198 Example 3.2: Cyberbullying 198 Exploration 3.2: How Mobile Are We? 203 Section 3.3: 2SD and Theory-Based Confidence Intervals for a Single Mean 207 Example 3.3: Used Cars 207 Exploration 3.3: Sleepless Nights? (cont.) 210 Section 3.4: Factors That Affect the Width of a Confidence Interval 213 Example 3.4: American Cat Ownership 214 Exploration 3.4A: Holiday Spending Habits 216 Exploration 3.4B: Reese’s Pieces 218 4 Causation: Can We Say What Caused the Effect? 245 Section 4.1: Association and Confounding 246 Example 4.1: Night Lights and Nearsightedness 247 Exploration 4.1: Home Court Disadvantage? 250 Section 4.2: Observational Studies Versus Experiments 252 Example 4.2: Lying on the Internet 253 Exploration 4.2: Have a Nice Trip 257 Unit 2 Comparing Two Groups 278 5 Comparing Two Proportions 279 Section 5.1: Comparing Two Groups: Categorical Response 280 Example 5.1: Buckling Up? 280 Exploration 5.1: Murderous Nurse? 285 Section 5.2: Comparing Two Proportions: Simulation-Based Approach 288 Example 5.2: Swimming with Dolphins 289 Exploration 5.2: Is Yawning Contagious? 297 Section 5.3: Comparing Two Proportions: Theory-Based Approach 304 Example 5.3: Parents’ Smoking Status and Their Babies’ Sex 305 Exploration 5.3: Donating Blood 311 6 Comparing Two Means 346 Section 6.1: Comparing Two Groups: Quantitative Response 347 Example 6.1: Geyser Eruptions 347 Exploration 6.1: Cancer Pamphlets 350 Section 6.2: Comparing Two Means: Simulation-Based Approach 354 Example 6.2: Dung Beetles 354 Exploration 6.2: Lingering Effects of Sleep Deprivation 363 Section 6.3: Comparing Two Means: Theory-Based Approach 369 Example 6.3: Violent Video Games and Aggression 369 Exploration 6.3: Close Friends 378 7 Paired Data: One Quantitative Variable 407 Section 7.1: Paired Designs 408 Example 7.1: Can You Study with Music Blaring? 408 Exploration 7.1: Rounding First Base 411 Section 7.2: Simulation-Based Approach to Analyzing Paired Data 413 Example 7.2: Rounding First Base (cont.) 414 Exploration 7.2: Exercise and Heart Rate 420 Section 7.3: Theory-Based Approach to Analyzing Data from Paired Samples 425 Example 7.3: Dad Jokes? 425 Exploration 7.3: Comparing Auction Formats 431 Unit 3 Analyzing More General Situations 456 8 Comparing More Than Two Proportions 458 Section 8.1: Comparing Multiple Proportions: Simulation-Based Approach 459 Example 8.1: Coming to a Stop 460 Exploration 8.1: Recruiting Organ Donors 466 Section 8.2: Comparing Multiple Proportions: Theory-Based Approach 470 Example 8.2: Sham Acupuncture 471 Exploration 8.2A: Conserving Hotel Towels 476 Exploration 8.2B: Nearsightedness and Night Lights Revisited 480 Section 8.3: Chi-Square Goodness-of-Fit Test 484 Example 8.3: Fair Die? 484 Exploration 8.3: Are Birthdays Equally Distributed Throughout the Week? 490 9 Comparing More Than Two Means 519 Section 9.1: Comparing Multiple Means: Simulation- Based Approach 520 Example 9.1: Comprehending Ambiguous Prose 520 Exploration 9.1: Exercise and Brain Volume 525 Section 9.2: Comparing Multiple Means: Theory-Based Approach 529 Example 9.2: Recalling Ambiguous Prose 530 Exploration 9.2: Comparing Popular Diets 538 10 Two Quantitative Variables 565 Section 10.1: Two Quantitative Variables: Scatterplots and Correlation 566 Example 10.1: Why Whales Are Big, but Not Bigger 567 Exploration 10.1: Height and Winning at Tennis 571 Section 10.2: Inference for the Correlation Coefficient: Simulation-Based Approach 576 Example 10.2: Exercise Intensity and Mood Changes 576 Exploration 10.2: Draft Lottery 580 Section 10.3: Least Squares Regression 585 Example 10.3: Height and Winning at Tennis (cont.) 585 Exploration 10.3: Predicting Height from Footprints 590 Section 10.4: Inference for the Regression Slope: Simulation-Based Approach 596 Example 10.4: Do Students Who Spend More Time in Non-Academic Activities Tend to Have Lower GPAs? 596 Exploration 10.4: Predicting Brain Density from Number of Facebook Friends 599 Section 10.5: Inference for the Regression Slope: Theory-Based Approach 601 Example 10.5A: Predicting Heart Rate from Body Temperature 602 Example 10.5B: Smoking and Drinking 606 Exploration 10.5: Predicting Brain Density from Number of Facebook Friends (cont.) 608 Unit 4 Probability (Online) 11-1 11 Modeling Randomness 11-2 Section 11.1: Basics of Probability 11-3 Example 11.1: Random Ice Cream Prices 11-3 Exploration 11.1: Random Babies 11-8 Section 11.2: Probability Rules 11-10 Example 11.2: Watching Films 11-11 Exploration 11.2: Random Ice Cream Prices (cont.) 11-15 Section 11.3: Conditional Probability and Independence 11-19 Example 11.3: Watching Films Revisited 11-20 Exploration 11.3A: College Admissions 11-25 Exploration 11.3B: Rare Disease Testing 11-28 Section 11.4: Discrete Random Variables 11-30 Example 11.4: A Game of Chance 11-30 Exploration 11.4: Traffic Lights 11-35 Section 11.5: Random Variable Rules 11-38 Example 11.5: A Game of Chance Revisited 11-38 Exploration 11.5: Skee-Ball 11-45 Section 11.6: Binomial and Geometric Random Variables 11-50 Example 11.6: Time to Leave the Nest? 11-52 Exploration 11.6: Clueless Quiz 11-59 Section 11.7: Continuous Random Variables and Normal Distributions 11-63 Example 11.7: Heights of Adult Women 11-65 Exploration 11.7A: Birthweights 11-69 Exploration 11.7B: Run, Girl, Run! 11-71 Section 11.8: Revisiting Theory-Based Approximations of Sampling Distributions 11-72 Example 11.8A: Time to Leave the Nest Revisited 11-74 Example 11.8B: Intelligence Test 11-75 Exploration 11.8A: Racket Spinning 11-77 Exploration 11.8B: Random Ice Cream Prices (cont.) 11-77 Appendix A Calculation Details 645 Appendix B Stratified and Cluster Samples 662 Solutions to Selected Exercises 666 Index 728