| |
| |
| |
An Introduction to Statistics and Research Design: The Elements of Statistical Reasoning | |
| |
| |
Two Branches of Statistics: Growing Our Knowledge about Human Behavior | |
| |
| |
Descriptive Statistics: Organizing, Summarizing, and Communicating Numerical Information | |
| |
| |
Inferential Statistics: Using Samples to Draw Conclusions about a Population | |
| |
| |
Distinguishing Between a Sample and a Population | |
| |
| |
Variables: Transforming Observations into Numbers | |
| |
| |
Independent and Dependent Variables: The Main Ingredients of Statistical Thinking | |
| |
| |
Putting Variables to Work: Independent, Dependent, and Confounding Variables | |
| |
| |
Developing and Assessing Variables: The Reliability and Validity of Tests | |
| |
| |
An Introduction to Hypothesis Testing: From Hunch to Hypothesis | |
| |
| |
Types of Research Designs: Experiments, Non-Experiments, and Quasi-Experiments | |
| |
| |
Experiments and Causality: Control the Confounding Variables | |
| |
| |
Research Designs Other than Experiments: Non-Experiments and Quasi-Experiments | |
| |
| |
One Goal, Two Strategies: Between-subjects Designs vs. Within-subjects Designs | |
| |
| |
Curiosity, Joy, and the Art of Research Design | |
| |
| |
Digging Deeper Into the Data: Variations on Standard Research Designs | |
| |
| |
Outlier Analyses: Does the Exception Prove the Rule? | |
| |
| |
Archival Studies: When the Data Already Exist | |
| |
| |
| |
Descriptive Statistics: Organizing, Summarizing, and Graphical Individual Variables | |
| |
| |
Organizing Our Data: A First Step in Identifying Patterns | |
| |
| |
Distributions: Four Different Ways to Describe Just One Variable | |
| |
| |
Applying Visual Depictions of Data: Generating Research Questions | |
| |
| |
Central Tendency: Determining the Typical Score | |
| |
| |
The Need for Alternative Measures of Central Tendency: Bipolar Disorder | |
| |
| |
Mean: The Arithmetic Average | |
| |
| |
Median: The Middle Score | |
| |
| |
Mode: The Most Common Score | |
| |
| |
The Effect of Outliers on Measures of Central Tendency | |
| |
| |
An Early Lesson in Lying With Statistics: Which Central Tendency is "Best?" | |
| |
| |
Measures of Variability: Everyone Can't Be "Typical" | |
| |
| |
Range: From the Lowest to the Highest Score | |
| |
| |
Variance: The First Step in Calculating Standard Deviation | |
| |
| |
Standard Deviation: Variation from the Mean | |
| |
| |
Shapes of Distributions: Applying the Tools of Descriptive Statistics | |
| |
| |
Normal Distributions: The Silent Power Behind Statistics | |
| |
| |
Skewed Distributions: When Our Data Are Not Symmetrical | |
| |
| |
Bimodal and Multimodal Distributions: Identifying Distinctive Populations | |
| |
| |
Kurtosis and Distributions: Tall and Skinny Versus Short and Wide | |
| |
| |
Digging Deeper into the Data: Alternate Approaches to Descriptive Statistics | |
| |
| |
The Interquartile Range: An Alternative to the Range | |
| |
| |
Statistics that Don't Focus on the Mean: Letting the Distribution Guide our Choice of Statistics | |
| |
| |
| |
Visual Displays of Data: Graphs That Tell a Story | |
| |
| |
Uses of Graphs: Clarifying Danger, Exposing Lies, and Gaining Insight | |
| |
| |
Graphing in the Information Age: A Critical Skill | |
| |
| |
"The Most Misleading Graph Ever Published": The Cost and Quality of Higher Education | |
| |
| |
"The Best Statistical Graph Ever Created": Napoleon's Disastrous March to Moscow | |
| |
| |
Common Types of Graphs: A Graph Designer's Building Blocks | |
| |
| |
Scatterplots: Observing Every Data Point | |
| |
| |
Line Graphs: Searching for Trends | |
| |
| |
Bar Graphs: An Efficient Communicator | |
| |
| |
Pictorial Graphs: Choosing Clarity over Cleverness | |
| |
| |
Pie Charts: Are Pie Charts Pass�©? | |