1.1 The Purpose of Statistics
1.2 Experiments and Variables
Quasi-Experimental Designs
Explanatory and Outcome Variables
1.3 Levels of Measurement
1.4 The Language of Statistics
1.5 Statistical Notation and Rounding
2.1 Frequency Distributions
Ungrouped vs. Grouped Frequency Distributions
Ungrouped Frequency Distributions
Grouped Frequency Distributions
2.2 Discrete Numbers and Continuous Numbers
2.3 Graphing Frequency Distributions
2.4 Shapes of Frequency Distributions
3.1 Central Tendency
Choosing a Measure of Central Tendency
3.2 Variability
Range and Interquartile Range
Variability in Populations
Population Standard Deviation
Calculating Variance and Standard Deviation for a Sample
4.1 Standard Scores (z Scores)
4.2 The Normal Distribution
5.1 Sampling and Sampling Error
5.2 Sampling Distributions and the Central Limit Theorem
The Central Limit Theorem
5.3 The 95% Confidence Interval for a Population Mean
6.1 The Logic of Hypothesis Testing
Ten Facts About Hypotheses and Hypothesis Testing
6.2 Hypothesis Testing in Action
The Six Steps of Hypothesis Testing
6.3 Type I Error, Type II Error, Beta, and Power
7.1 Calculating the Single-Sample t Test
The Six Steps of Hypothesis Testing
7.2 Interpreting the Single-Sample t Test
8.1 Types of Two-Sample t Tests
8.2 Calculating the Independent-Samples t Test
8.3 Interpreting the Independent-Samples t Test
Was the Null Hypothesis Rejected?
How Wide Is the Confidence Interval?
9.2 Calculating the Paired-Samples t Test
9.3 Interpreting the Paired-Samples t Test
Was the Null Hypothesis Rejected?
How Wide Is the Confidence Interval and How Big Is the Effect?
10.1 Introduction to Analysis of Variance
Analysis of Variance Terminology
How ANOVA Uses Variability
10.2 Calculating Between-Subjects, One-Way ANOVA
10.3 Interpreting Between-Subjects, One-Way ANOVA
Was the Null Hypothesis Rejected?
11.1 Introduction to Repeated-Measures ANOVA
11.2 Calculating One-Way, Repeated-Measures ANOVA
11.3 Interpreting One-Way, Repeated-Measures ANOVA
Was the Null Hypothesis Rejected?
Where Are the Effects and What Is Their Direction?
Appendix
Calculating Sums of Squares for One-Way Repeated-Measures ANOVA
12.1 Introduction to Two-Way ANOVA
12.2 Calculating a Between-Subjects, Two-Way ANOVA
12.3 Interpreting a Between-Subjects, Two-Way ANOVA
Were the Null Hypotheses Rejected?
Where Are the Effects, and What Is Their Direction?
Appendix
Calculating Sums of Squares for Between-Subjects, Two-Way ANOVA
13.1 Introduction to the Pearson Correlation Coefficient
Defining Pearson r and Relationship
Correlation, Causation, and Association
Visualizing Relationships
Strength of Relationships
Correlations Defined by z Scores
Quantifying Relationships
Direction of the Relationship
Conditions Affecting Pearson r
13.2 Calculating the Pearson Correlation Coefficient
13.3 Interpreting the Pearson Correlation Coefficient
Was the Null Hypothesis Rejected?
How Wide Is the Confidence Interval?
13.4 Calculating a Partial Correlation
14.1 Simple Linear Regression
Using a Regression Line for Prediction
How to Judge Whether Prediction Is Good
The Linear Regression Equation
14.2 Errors in Regression
15.1 Introduction to Nonparametric Tests
15.2 The Chi-Square Goodness-of-Fit Test
Was the Null Hypothesis Rejected?
What Is the Direction of the Results?
15.3 Calculating the Chi-Square Test of Independence
15.4 Interpreting the Chi-Square Test of Independence
Was the Null Hypothesis Rejected?
What Is the Direction of the Difference?
15.5 Other Nonparametric Tests
The Spearman Rank-Order Correlation Coefficient
16.1 Review of Statistical Tasks
16.2 Descriptive Statistics
Describing Individual Cases
Describing a Sample of Cases
16.3 Hypothesis Tests I: Difference Tests
16.4 Hypothesis Tests II: Relationship Tests