Table Of Contents:

• Preface
• About the Authors
• Prologue
• PART I • GETTING STARTED
• Chapter 1: The Big Picture
• Models
• The Classical Statistical Model
• Designing Experiments and Analyzing Data
• Summary
• Questions Raised by the Use of the Classical Statistical Model
• Conceptual Exercises
• Chapter 2: Examining Our Data: An Introduction to Some of the Techniques of Exploratory Data Analysis
• Descriptive Statistics
• Histograms
• Exploratory Data Analysis
• Quantile Plots
• Stem-and-Leaf Displays
• Letter-Value Displays
• Box Plots
• Did My Data Come From a Normal Distribution?
• Why Should We Care About Looking at Our Data?
• Summary
• Conceptual Exercises
• PART II • THE BEHAVIOR OF DATA
• Chapter 3: Properties of Distributions: The Building Blocks of Statistical Inference
• The Effects of Adding a Constant or Multiplying by a Constant
• The Standard Score Transformation
• The Effects of Adding or Subtracting Scores From Two Different Distributions
• The Distribution of Sample Means
• The Central Limit Theorem
• Averaging Means and Variances
• Expected Value
• Theorems on Expected Value
• Summary
• Conceptual Exercises
• PART III • THE BASICS OF STATISTICAL INFERENCE: DRAWING CONCLUSIONS FROM OUR DATA
• Chapter 4: Estimating Parameters of Populations From Sample Data
• Statistical Inference With the Classical Statistical Model
• Criteria for Selecting Estimators of Population Parameters
• Maximum Likelihood Estimation
• Confidence Intervals
• Beyond Normal Distributions and Estimating Population Means
• Summary
• Conceptual Exercises
• Chapter 5: Resistant Estimators of Parameters
• A Closer Look at Sampling From Non-Normal Populations
• The Sample Mean and Sample Median Are L-Estimators
• Measuring the Influence of Outliers on Estimates of Location and Spread
• ?-Trimmed Means as Resistant and Efficient Estimators of Location
• Winsorizing: Another Way to Create a Resistant Estimator of Location
• Applying These Resistant Estimators to Our Data
• Resistant Estimators of Spread
• Applying These Resistant Estimators to Our Data (Part 2)
• M-Estimators: Another Approach to Finding Resistant Estimators of Location
• Which Estimator of Location Should I Use?
• Resampling Methods for Constructing Confidence Intervals
• A Final Caveat
• Summary
• Conceptual Exercises
• Chapter 6: General Principles of Hypothesis Testing
• Experimental and Statistical Hypotheses
• Estimating Parameters
• The Criterion for Evaluating Our Statistical Hypotheses
• Creating Our Test Statistic
• Drawing Conclusions About Our Null Hypothesis
• But Suppose H0 Is False?
• Errors in Hypothesis Testing
• Power and Power Functions
• The Use of Power Functions
• p-Values, a, and Alpha (Type I) Errors: What They Do and Do Not Mean
• A Word of Caution About Attempting to Estimate the Power of a Hypothesis Test After the Data Have Been Collected
• Is It Ever Appropriate to Use a One-Tailed Hypothesis Test?
• What Should We Mean When We Say Our Results Are Statistically Significant?
• A Final Word
• Summary
• Conceptual Exercises
• PART IV • SPECIFIC TECHNIQUES TO ANSWER SPECIFIC QUESTIONS
• Chapter 7: The Independent Groups t-Tests for Testing for Differences Between Population Means
• Student’s t-test
• Distribution of the Independent Groups t-Statistic when H0 Is True
• Distribution of the Independent Groups t-Statistic When H0 Is False
• Factors That Affect the Power of the Independent Groups t-Test
• The Assumption Behind the Homogeneity of Variance Assumption
• Graphical Methods for Comparing Two Groups
• Suppose the Population Variances Are Not Equal?
• Standardized Group Differences as Estimators of Effect Size
• Robust Hypothesis Testing
• Resistant Estimates of Effect Size
• Summary
• Conceptual Exercises
• Chapter 8: Testing Hypotheses When the Dependent Variable Consists of Frequencies of Scores in Various Categories
• Classifying Data
• Testing Hypotheses When the Dependent Variable Consists of Only Two Possibilities
• The Binomial Distribution
• Testing Hypotheses About the Parameter p in a Binomial Experiment
• The Normal Distribution Approximation to the Binomial Distribution
• Testing Hypotheses About the Difference Between Two Binomial Parameters (p1 – p2)
• Testing Hypotheses in Which the Dependent Variable Consists of Two or More Categories
• Summary
• Conceptual Exercises
• Chapter 9: The Randomization/Permutation Model: An Alternative to the Classical Statistical Model for Testing Hypotheses About Treatment Effects
• The Assumptions Underlying the Classical Statistical Model
• The Assumptions Underlying the Randomization Model
• Hypotheses for Both Models
• The Exact Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior
• The Approximate Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior
• Using the Randomization Model to Investigate Possible Effects of Treatments
• Single-Participant Experimental Designs
• Summary
• Conceptual Exercises
• Additional Resources
• Chapter 10: Exploring the Relationship Between Two Variables: Correlation
• Measuring the Degree of Relationship Between Two Interval-Scale Variables
• Randomization (Permutation) Model for Testing Hypotheses About the Relationship Between Two Variables
• The Bivariate Normal Distribution Model for Testing Hypotheses About Population Correlations
• Creating a Confidence Interval for the Population Correlation Using the Bivariate Normal Distribution Model
• Bootstrap Confidence Intervals for the Population Correlation
• Unbiased Estimators of the Population Correlation
• Robust Estimators of Correlation
• Assessing the Relationship Between Two Nominal Variables
• The Fisher Exact Probability Test for 2 x 2 Contingency Tables With Small Sample Sizes
• Correlation Coefficients for Nominal Data in Contingency Tables
• Summary
• Conceptual Exercises
• Chapter 11: Exploring the Relationship Between Two Variables: The Linear Regression Model
• Assumptions for the Linear Regression Model
• Estimating Parameters With the Linear Regression Model
• Regression and Prediction
• Variance and Correlation
• Testing Hypotheses With the Linear Regression Model
• Summary
• Conceptual Exercises
• Chapter 12: A Closer Look at Linear Regression
• The Importance of Looking at Our Data
• Using Residuals to Check Assumptions
• Testing Whether the Relationship Between Two Variables Is Linear
• The Correlation Ratio: An Alternate Way to Measure the Degree of Relationship and Test for a Linear Relationship
• Where Do We Go From Here?
• When the Relationship Is Not Linear
• The Effects of Outliers on Regression
• Robust Alternatives to the Method of Least Squares
• A Quick Peek at Multiple Regression
• Summary
• Conceptual Exercises
• Chapter 13: Another Way to Scale the Size of Treatment Effects
• The Point Biserial Correlation Coefficient and the t-Test
• Advantages and Disadvantages of Estimating Effect Sizes With Correlation Coefficients or Standardized Group Difference Measures
• Confidence Intervals for Effect Size Estimates
• Final Comments on the Use of Effect Size Estimators
• Summary
• Conceptual Exercises
• Chapter 14: Analysis of Variance for Testing for Differences Between Population Means
• What Are the Sources of Variation in Our Experiments?
• Experimental and Statistical Hypotheses
• Estimating Variances
• When There Are More Than Two Conditions in Your Experiment
• Assumptions for Analysis of Variance
• Testing Hypotheses About Differences Among Population Means With Analysis of Variance
• Factors That Affect the Power of the F-Test in Analysis of Variance
• Relational Effect Size Measures for Analysis of Variance
• Randomization Tests for Testing for Differential Effects of Three or More Treatments
• Using ANOVA to Study the Effects of More Than One Factor on Behavior
• Partitioning Variance for a Two-Factor Analysis of Variance
• Testing Hypotheses With Two-Factor Analysis of Variance
• Testing Hypotheses About Differences Among Population Means With Analysis of Variance
• Dealing With Unequal Sample Sizes in Factorial Designs
• Summary
• Conceptual Exercises
• Chapter 15: Multiple Regression and Beyond
• Overview of the General Linear Model Approach
• Regression
• Simple Versus Multiple Regression
• Multiple Regression
• Types of Multiple Regression
• Interactions in Multiple Regression
• Continuous x Continuous Interactions
• Categorical x Continuous Interactions
• Categorical x Categorical Interactions: ANOVA Versus Regression
• Summary
• Conceptual Exercises
• Epilogue
• Appendices
• A. Some Useful Rules of Algebra
• B. Rules of Summation
• C. Logarithms
• D. The Inverse of the Cumulative Normal Distribution
• E. The Unit Normal Distribution
• F. The t-Distribution
• G. The Fisher r to zr Transformation
• H. Critical Values for F With Alpha = .05
• I. The Chi Square Distribution
• References
• Index