Transcript Sampling Distributions 8.1

A review of basic methods and terms
A preview of the CLT
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!
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
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)
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.