1. Sampling Distributions 8.1
A review of basic methods and terms
A preview of the CLT

2. Big Ideas Population = All measurements of interest
Sample = A subset of the measurements from the population
Random Sample = A representative sample… a sample that accurately reflects the population… its what we are interested in and worked so hard to get in projects
Draw The Picture!

3. Sample Statistics and Population Parameters
A Statistic = a numerical descriptive measure of a sample… a # that describes a sample… x bar, s, s squared, p hat
A Paramter = a numerical descriptive measure of the population… a # that describes the pop… mew, little sigma, sigma squared, rho, and others to be learned
Put symbols in the picture

4. Inference
We use a sample statistics to make inferences (conclusions, decisions) about population parameters when we don’t have access (usually by choice) to all the measurements in the entire population.
We make inferences by: Estimation (chapter 9/ confidence intervals) and Decision (chapters 10+/hypothesis testing)

5. So, where ya headed ?
Chs 1—4: Descriptive Statistics organize, summarize numbers
Chs 5—8: Probability Theory and Distributions
Chs 9—13: Inferential Statistics…Methods of using a sample to obtain reliable information about the population. We wont be certain that our results absolutely reflect the entire population…however, we will be pretty sure…highly confident…and we can describe likely differences.