To provide the reader with a unifying structure to understand statistics, data analysis, and inference that spans the enormity of the science in a way that is accessible to the layman.

Intended audience: graduate students who have not been exposed to statistics

- How to describe one variable (Univariate Descriptives)
- Describe data distributions via graphical (e.g., histograms) and summary statistics (e.g., mean, standard deviation) techniques that evaluate center and spread.

- How to describe how two variables relate (Bivariate associations)
- Describe bivariate relationships via graphical (e.g., scatterplots) and summary statistics (e.g., covariance, correlation) techniques that evaluate the strength of linear associations.

- How to construct graphics to show relationships between variables (Structural Equation Modeling Diagrams)
- Show hypothesized causal and associative relationships between variables through accepted SEM diagramming techniques.

- Sampling Distributions
- Describe the process of the creation of sampling distributions and describe their distributional properties (e.g., spread or standard error).

- Statistical language fluency
- Demonstrate mastery of statistical terminology (e.g., p-value, standard errors) through the application and the implication on the stories we can tell from data.

- Regression Betas
- Recite from memory the definition of an unstandardized and standardized beta in a linear regression context and apply its definition in understanding its implication in data analysis.

- Relate to traditional statistics
- Demonstrate mastery of how SEM techniques are equivalent to traditional statistical methodology (e.g., ANOVA, t-test, etc).

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