Table Of Contents:

• Preface
• Acknowledgments
• About the Author
• Chapter 1: A Gentle Introduction
• How Much Math Do I Need to Do Statistics?
• The General Purpose of Statistics: Understanding the World
• What Is a Statistician?
• Liberal and Conservative Statisticians
• Descriptive and Inferential Statistics
• Experiments Are Designed to Test Theories and Hypotheses
• Oddball Theories
• Bad Science and Myths
• Eight Essential Questions of Any Survey or Study
• On Making Samples Representative of the Population
• Experimental Design and Statistical Analysis as Controls
• The Language of Statistics
• On Conducting Scientific Experiments
• The Dependent Variable and Measurement
• Operational Definitions
• Measurement Error
• Measurement Scales: The Difference Between Continuous and Discrete Variables
• Types of Measurement Scales
• Rounding Numbers and Rounding Error
• Statistical Symbols
• Summary
• History Trivia: Achenwall to Nightingale
• Key Terms
• Chapter 1 Practice Problems
• Chapter 1 Test Yourself Questions
• SPSS Lesson 1
• Chapter 2: Descriptive Statistics: Understanding Distributions of Numbers
• The Purpose of Graphs and Tables: Making Arguments and Decisions
• A Summary of the Purpose of Graphs and Tables
• Graphical Cautions
• Frequency Distributions
• Shapes of Frequency Distributions
• Grouping Data Into Intervals
• Advice on Grouping Data Into Intervals
• The Cumulative Frequency Distribution
• Cumulative Percentages, Percentiles, and Quartiles
• Stem-and-Leaf Plot
• Non-normal Frequency Distributions
• On the Importance of the Shapes of Distributions
• Additional Thoughts About Good Graphs Versus Bad Graphs
• History Trivia: De Moivre to Tukey
• Key Terms
• Chapter 2 Practice Problems
• Chapter 2 Test Yourself Questions
• SPSS Lesson 2
• Chapter 3: Statistical Parameters: Measures of Central Tendency and Variation
• Measures of Central Tendency
• Choosing Among Measures of Central Tendency
• Klinkers and Outliers
• Uncertain or Equivocal Results
• Measures of Variation
• Correcting for Bias in the Sample Standard Deviation
• How the Square Root of x2 Is Almost Equivalent to Taking the Absolute Value of x
• The Computational Formula for Standard Deviation
• The Variance
• The Sampling Distribution of Means, the Central Limit Theorem, and the Standard Error of the Mean
• The Use of the Standard Deviation for Prediction
• Practical Uses of the Empirical Rule: As a Definition of an Outlier
• Practical Uses of the Empirical Rule: Prediction and IQ Tests
• Some Further Comments
• History Trivia: Fisher to Eels
• Key Terms
• Chapter 3 Practice Problems
• Chapter 3 Test Yourself Questions
• SPSS Lesson 3
• Chapter 4: Standard Scores, the z Distribution, and Hypothesis Testing
• Standard Scores
• The Classic Standard Score: The z Score and the z Distribution
• Calculating z Scores
• More Practice on Converting Raw Data Into z Scores
• Converting z Scores to Other Types of Standard Scores
• The z Distribution
• Interpreting Negative z Scores
• Testing the Predictions of the Empirical Rule With the z Distribution
• Why Is the z Distribution So Important?
• How We Use the z Distribution to Test Experimental Hypotheses
• More Practice With the z Distribution and T Scores
• Summarizing Scores Through Percentiles
• History Trivia: Karl Pearson to Egon Pearson
• Key Terms
• Chapter 4 Practice Problems
• Chapter 4 Test Yourself Questions
• SPSS Lesson 4
• Chapter 5: Inferential Statistics: The Controlled Experiment, Hypothesis Testing, and the z Distribution
• Hypothesis Testing in the Controlled Experiment
• Hypothesis Testing: The Big Decision
• How the Big Decision Is Made: Back to the z Distribution
• The Parameter of Major Interest in Hypothesis Testing: The Mean
• Nondirectional and Directional Alternative Hypotheses
• A Debate: Retain the Null Hypothesis or Fail to Reject the Null Hypothesis
• The Null Hypothesis as a Nonconservative Beginning
• The Four Possible Outcomes in Hypothesis Testing
• Significance Levels
• Significant and Nonsignificant Findings
• Trends, and Does God Really Love the .05 Level of Significance More Than the .06 Level?
• Directional or Nondirectional Alternative Hypotheses: Advantages and Disadvantages
• Did Nuclear Fusion Occur?
• Baloney Detection
• Conclusions About Science and Pseudoscience
• The Most Critical Elements in the Detection of Baloney in Suspicious Studies and Fraudulent Claims
• Can Statistics Solve Every Problem?
• Probability
• History Trivia: Egon Pearson to Karl Pearson
• Key Terms
• Chapter 5 Practice Problems
• Chapter 5 Test Yourself Questions
• SPSS Lesson 5
• Chapter 6: An Introduction to Correlation and Regression
• Correlation: Use and Abuse
• A Warning: Correlation Does Not Imply Causation
• Another Warning: Chance Is Lumpy
• Correlation and Prediction
• The Four Common Types of Correlation
• The Pearson Product–Moment Correlation Coefficient
• Testing for the Significance of a Correlation Coefficient
• Obtaining the Critical Values of the t Distribution
• If the Null Hypothesis Is Rejected
• Representing the Pearson Correlation Graphically: The Scatterplot
• Fitting the Points With a Straight Line: The Assumption of a Linear Relationship
• Interpretation of the Slope of the Best-Fitting Line
• The Assumption of Homoscedasticity
• The Coefficient of Determination: How Much One Variable Accounts for Variation in Another Variable—The Interpretation of r2
• Quirks in the Interpretation of Significant and Nonsignificant Correlation Coefficients
• Linear Regression
• Reading the Regression Line
• Final Thoughts About Multiple Regression Analyses: A Warning About the Interpretation of the Significant Beta Coefficients
• Spearman’s Correlation
• Significance Test for Spearman’s r
• Ties in Ranks
• Point-Biserial Correlation
• Testing for the Significance of the Point-Biserial Correlation Coefficient
• Phi (F) Correlation
• Testing for the Significance of Phi
• History Trivia: Galton to Fisher
• Key Terms
• Chapter 6 Practice Problems
• Chapter 6 Test Yourself Questions
• SPSS Lesson 6
• Chapter 7: The t Test for Independent Groups
• The Statistical Analysis of the Controlled Experiment
• One t Test but Two Designs
• Assumptions of the Independent t Test
• The Formula for the Independent t Test
• You Must Remember This! An Overview of Hypothesis Testing With the t Test
• What Does the t Test Do? Components of the t Test Formula
• What If the Two Variances Are Radically Different From One Another?
• A Computational Example
• Marginal Significance
• The Power of a Statistical Test
• Effect Size
• The Correlation Coefficient of Effect Size
• Another Measure of Effect Size: Cohen’s d
• Confidence Intervals
• Estimating the Standard Error
• History Trivia: Gosset and Guinness Brewery
• Key Terms
• Chapter 7 Practice Problems
• Chapter 7 Test Yourself Questions
• SPSS Lesson 7
• Chapter 8: The t Test for Dependent Groups
• Variations on the Controlled Experiment
• Assumptions of the Dependent t Test
• Why the Dependent t Test May Be More Powerful Than the Independent t Test
• How to Increase the Power of a t Test
• Drawbacks of the Dependent t Test Designs
• One-Tailed or Two-Tailed Tests of Significance
• Hypothesis Testing and the Dependent t Test: Design 1
• Design 1 (Same Participants or Repeated Measures): A Computational Example
• Design 2 (Matched Pairs): A Computational Example
• Design 3 (Same Participants and Balanced Presentation): A Computational Example
• History Trivia: Fisher to Pearson
• Key Terms
• Chapter 8 Practice Problems
• Chapter 8 Test Yourself Questions
• SPSS Lesson 8
• Chapter 9: Analysis of Variance (ANOVA): One-Factor Completely Randomized Design
• A Limitation of Multiple t Tests and a Solution
• The Equally Unacceptable Bonferroni Solution
• The Acceptable Solution: An Analysis of Variance
• The Null and Alternative Hypotheses in ANOVA
• The Beauty and Elegance of the F Test Statistic
• The F Ratio
• How Can There Be Two Different Estimates of Within-Groups Variance?
• ANOVA Designs
• ANOVA Assumptions
• Pragmatic Overview
• What a Significant ANOVA Indicates
• A Computational Example
• Degrees of Freedom for the Numerator
• Degrees of Freedom for the Denominator
• Determining Effect Size in ANOVA: Omega Squared (w2)
• Another Measure of Effect Size: Eta (h)
• History Trivia: Gosset to Fisher
• Key Terms
• Chapter 9 Practice Problems
• Chapter 9 Test Questions
• Chapter 9 Test Yourself Questions
• SPSS Lesson 9
• Chapter 10: After a Significant ANOVA: Multiple Comparison Tests
• Conceptual Overview of Tukey’s Test
• Computation of Tukey’s HSD Test
• What to Do If the Number of Error Degrees of Freedom Is Not Listed in the Table of Tukey’s q Values
• Determining What It All Means
• Warning!
• On the Importance of Nonsignificant Mean Differences
• Final Results of ANOVA
• Quirks in Interpretation
• Tukey’s With Unequal Ns
• Key Terms
• Chapter 10 Practice Problems
• Chapter 10 Test Yourself Questions
• SPSS Lesson 10
• Chapter 11: Analysis of Variance (ANOVA): One-Factor Repeated-Measures Design
• The Repeated-Measures ANOVA
• Assumptions of the One-Factor Repeated-Measures ANOVA
• Computational Example
• Determining Effect Size in ANOVA
• Key Terms
• Chapter 11 Practice Problems
• Chapter 11 Test Yourself Questions
• SPSS Lesson 11
• Chapter 12: Factorial ANOVA: Two-Factor Completely Randomized Design
• Factorial Designs
• The Most Important Feature of a Factorial Design: The Interaction
• Fixed and Random Effects and In Situ Designs
• The Null Hypotheses in a Two-Factor ANOVA
• Assumptions and Unequal Numbers of Participants
• Computational Example
• Key Terms
• Chapter 12 Practice Problems
• Chapter 12 Test Yourself Problems
• SPSS Lesson 12
• Chapter 13: Post Hoc Analysis of Factorial ANOVA
• Main Effect Interpretation: Gender
• Why a Multiple Comparison Test Is Unnecessary for a Two-Level Main Effect, and When Is a Multiple Comparison Test Necessary?
• Main Effect: Age Levels
• Multiple Comparison Test for the Main Effect for Age
• Warning: Limit Your Main Effect Conclusions When the Interaction Is Significant
• Multiple Comparison Tests
• Interpretation of the Interaction Effect
• Final Summary
• Writing Up the Results Journal Style
• Language to Avoid
• Exploring the Possible Outcomes in a Two-Factor ANOVA
• Determining Effect Size in a Two-Factor ANOVA
• History Trivia: Fisher and Smoking
• Key Terms
• Chapter 13 Practice Problems
• Chapter 13 Test Yourself Questions
• SPSS Lesson 13
• Chapter 14: Factorial ANOVA: Additional Designs
• The Split-Plot Design
• Overview of the Split-Plot ANOVA
• Computational Example
• Two-Factor ANOVA: Repeated Measures on Both Factors Design
• Overview of the Repeated-Measures ANOVA
• Computational Example
• Key Terms and Definitions
• Chapter 14 Practice Problems
• Chapter 14 Test Yourself Questions
• SPSS Lesson 14
• Chapter 15: Nonparametric Statistics: The Chi-Square Test and Other Nonparametric Tests
• Overview of the Purpose of Chi-Square
• Overview of Chi-Square Designs
• Chi-Square Test: Two-Cell Design (Equal Probabilities Type)
• The Chi-Square Distribution
• Assumptions of the Chi-Square Test
• Chi-Square Test: Two-Cell Design (Different Probabilities Type)
• Interpreting a Significant Chi-Square Test for a Newspaper
• Chi-Square Test: Three-Cell Experiment (Equal Probabilities Type)
• Chi-Square Test: Two-by-Two Design
• What to Do After a Chi-Square Test Is Significant
• When Cell Frequencies Are Less Than 5 Revisited
• Other Nonparametric Tests
• History Trivia: Pearson and Biometrika
• Key Terms
• Chapter 15 Practice Problems
• Chapter 15 Test Yourself Questions
• SPSS Lesson 15
• Chapter 16: Other Statistical Topics, Parameters, and Tests
• Big Data
• Health Science Statistics
• Additional Statistical Analyses and Multivariate Statistics
• A Summary of Multivariate Statistics
• Coda
• Key Terms
• Chapter 16 Practice Problems
• Chapter 16 Test Yourself Questions
• Appendix A: z Distribution
• Appendix B: t Distribution
• Appendix C: Spearman’s Correlation
• Appendix D: Chi-Square ?2 Distribution
• Appendix E: F Distribution
• Appendix F: Tukey’s Table
• Appendix G: Mann–Whitney U Critical Values
• Appendix H: Wilcoxon Signed-Rank Test Critical Values
• Appendix I: Answers to Odd-Numbered Test Yourself Questions
• Glossary
• References
• Index