Main Body

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

Homework Assignments

Appendices

# Learning Objectives

• 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
• 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
• 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
• 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 