# Table of Contents and Learning Objectives

# Table of Contents

- Title Page with license information

**Main Body**

- Chapter 1 Why We Need Statistics and Displaying Data Using Tables and Graphs
- Chapter 2 Central Tendency and Variability
- Chapter 3 Z-scores and the Normal Curve
- Chapter 4 Probability, Inferential Statistics, and Hypothesis Testing
- Chapter 5 Single Sample Z-test and t-test
- Chapter 6 Dependent t-test
- Chapter 7 Independent Means t-test
- Chapter 8 Analysis of Variance, Planned Contrasts and Posthoc Tests
- Chapter 9 Factorial ANOVA and Interaction Effects
- Chapter 10 Correlation and Regression
- Chapter 11 Beyond Hypothesis Testing
- Afterword

**Homework Assignments**

- Homework Chapter 1
- Homework Chapter 2
- Homework Chapter 3
- Homework Chapter 4
- Homework Chapter 5
- Homework Chapter 6
- Homework Chapter 7
- Homework Chapter 8
- Homework Chapter 9
- Homework Chapter 10
- Homework Chapter 11

**Appendices**

**Learning Objectives**

Chapter 1 Why We Need Statistics and Displaying Data Using Tables and Graphs

- articulate the purpose of a course introducing statistical principles and techniques
- supply examples of situations in which data analysis techniques may be necessary
- define descriptive and inferential statistics, variable, value, and score
- distinguish between two levels of measurement and identify the appropriate techniques for summarizing different types of data
- generate frequency tables
- graph a dataset using a histogram, bar graph, or pie chart
- describe a distribution shape in terms of peaks and symmetry

Chapter 2 Central Tendency and Variability

- define and determine mean, median, and mode, as three options to determine central tendency
- distinguish among the measures of central tendency and the circumstances under which each is suitable
- define and determine variance and standard deviation, as two options to determine variability
- interpret standard deviation

Chapter 3 Z-scores and the Normal Curve

- transform scores in any numeric dataset, using any scale, into the standard metric of Z-scores
- interpret Z-scores and apply them for comparison of scores within and between datasets, including data measured on different scales
- define and characterize the normal curve model
- associate Z-scores with areas under the normal curve
- define percentiles and determine Z-scores and raw scores that form the border of percentiles using the normal curve model

Chapter 4 Probability, Inferential Statistics, and Hypothesis Testing

- determine simple probabilities
- appreciate the importance of probability and ubiquity of human failings in the realm of probability
- connect probability to percentiles, areas under the normal curve, and the logic of inferential statistics such as hypothesis testing
- define and distinguish between population and sample
- articulate the central tendency theorem and describe its implications for the normality assumption in inferential statistics
- outline and apply the steps of hypothesis testing

Chapter 5 Single Sample Z-test and t-test

- define and identify Type I and Type II errors
- define and characterize the distribution of means as compared to the distribution of individuals
- determine the mean and standard deviation of the distribution of means based on the characteristics of the distribution of individuals
- conduct a hypothesis test using the single sample Z-test
- define, determine, and interpret a p-value
- articulate a conclusion in plain language from an test of statistical significance
- define and determine degrees of freedom
- articulate the logic behind the sample size correction for sample-based estimates of variance
- describe the difference between t-distribution shapes with varying degrees of freedom
- conduct a hypothesis test using the single sample t-test
- identify scenarios in which a single sample Z-test or t-test is appropriate

Chapter 6 Dependent t-test

- identify and describe repeated measures and matched pairs research designs
- conduct a hypothesis test using the dependent means t-test
- identify scenarios in which a dependent means t-test is appropriate

Chapter 7 Independent Means t-test

- identify and describe classical experimental research designs
- identify the (normal curve and homoscedasticity) assumptions behind the independent means t-test
- conduct a hypothesis test using the independent means t-test
- identify scenarios in which an independent means t-test is appropriate

Chapter 8 Analysis of Variance, Planned Contrasts and Posthoc Tests

- define partitioning of variance and apply the concept to one-way Analysis of Variance
- define and identify factors and levels in research designs
- use graphing techniques to visualize data from a research design using more than 2 levels in a factor
- conduct a hypothesis test using one-way Analysis of Variance
- articulate reasons for conducting planned contrasts or post-hoc tests following ANOVA
- define experimentwise alpha level and articulate ways in which Bonferroni and Scheffé corrections address inflated risk of Type I error
- outline the procedure for conducting planned contrasts with Bonferroni correction
- outline the procedure for conducting posthoc tests with Scheffé correction
- identify scenarios in which a one-way ANOVA is appropriate

Chapter 9 Factorial ANOVA and Interaction Effects

- apply the concept of partitioning of variance to two-way Analysis of Variance
- describe factorial analysis and articulate its benefits and pitfalls
- describe research designs using ___ X ___ factor and level summaries
- conduct a hypothesis test using two-way Analysis of Variance
- identify scenarios in which a two-way ANOVA is appropriate
- identify and interpret main effects
- identify and interpret interactions

Chapter 10 Correlation and Regression

- define correlation and regression
- detect and describe linear correlation patterns using scatterplots
- define partitioning of covariance
- conduct a hypothesis test using correlation
- find the proportion of variance explained by a correlation
- identify scenarios in which a correlation is appropriate
- create a predictive model using a simple regression line
- articulate limits to accuracy and usefulness of regression models

Chapter 11 Beyond Hypothesis Testing

- define effect size, power, and confidence intervals
- articulate the importance of effect size and power analyses
- find and interpret Cohen’s d for a single-sample Z-test scenario
- identify the two major determinants of statistical power
- estimate and interpret power for a single-sample Z-test scenario
- construct confidence intervals for a single-sample Z-test scenario
- articulate similarities and differences between hypothesis testing and confidence interval procedures