Introduction. 1: Description. Populations, Distributions, and Samples. Measures of Central Tendency. Data Dispersion, Noise, and Error. Graphics. 2: Inference. Comparing a Sample Mean to a Population with Known Mean and Variance - The One Sample z-Test. Comparing a Sample Mean to a Population with Known Mean and Unknown Variance - The One Sample t-Test. Comparing Before and After Data - The Two Sample Paired t-Test. Comparing Two Means - The Two Sample Unpaired t-Test. Comparing Three or More Means - The One Way Analysis of Variance. Comparing Two or More Proportions: Proportions Tests and Chi-Square (chi2). Distribution-Free Measures: Non-Parametric Tests. 3: Estimation. Data Relationships: Association and Correlation. Data Relationships: Mathematical Models and Linear Regression. Complex Data Relationships: Mathematical Models and Non-Linear Regression. 4: Design of a Statistical Experiment. Index.