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Objective:

To develop a deeper understanding understanding statistical analysis, especially the use of sampling distributions and inferential statistics in identifying correlations, making estimates and testing hypotheses.

Contents:

Research Design: Scientific Method, Measurement, Basics of Data Collection, Sampling Bias, Experimental Designs, Causation

Sampling Distributions: Sampling Distribution of the Mean, Difference Between Means, Sampling Distribution of Pearson’s r

Estimation theory: Introduction, Degrees of Freedom, Characteristics of Estimators: Bias and Precision, Point Estimates and Confidence Intervals, t Distribution, Difference between Means, Correlation, Proportion

Hypothesis Testing: Significance Testing, Type I and Type II Errors, One- and Two-Tailed Tests, Interpreting Significant Results, Interpreting Non-Significant Results, Steps in Hypothesis Testing, Significance Testing and Confidence Intervals

Testing Means and Analysis of Variance: Single Mean, Difference between Two Means, Pairwise Comparisons Among Means, ANOVA Designs, Between-Subjects and Within-Subjects Factors, One-Factor and Multi-Factor ANOVA, Unequal Sample Sizes, Within-Subjects ANOVA

Textbook:

David M. Lane et al, Introduction to Statistics (Online Edition), developed by Rice University, University of Houston Clear Lake, and Tufts University, http://onlinestatbook.com/2/index.html.

References and Resources:

HyperStat Online Statistics Textbook, http://davidmlane.com/hyperstat/index.html

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