2. Producing Data Designing Samples

3. Experiments Deliberately change a variable in order to observe response Here you are actually INFLUENCING the response Only plausible way to show cause & effect You CAN control lurking variables to help minimize their effect

4. SRS (Simple Random Sample) A random sample where each individual has an equal opportunity to be chosen and each sample has an equal opportunity of being produced Random # TableCalculator Assign each item a # Given a starting pt, look for (__) digit #’s within the appropriate interval randInt(lowest#,highest#, # desired)

5. Pick 3 Random Citizens 13500 51807 42130 23171 randInt(1,12,3)

6. Alternative Sampling Methods Stratified Sampling Divide the population into categories (strata) and select a random sample from each category Cluster Sampling Divide the population into categories (clusters), randomly select one or more categories, and select EVERYONE within the chosen categories.

7. Pick 3 Random Citizens We’ll group them by age Here are the groupings!! randInt(1,4,1)

8. Pick 3 Random Citizens We’ll group them by location. Here are the groupings!! randInt(1,4,1) But this is a little different 1 2 3 4

9. Stratified Sampling The students have been asked by Governor Martin O’Malley to survey the state of Maryland to determine the level of participation by teenagers in the state’s public school system. Describe 2 ways to design a stratified sample for this study. 1)Split the state into Counties (Strata). Assign a number to each individual in each county and take a random sample of n people from each county. PROBLEMS??? 2) Split the state into Zip Codes (Strata). Assign a number to each individual in each zip and take a random sample of n people from each one. PROBLEMS??? How would you change these to be Cluster Sampling?

10. Try This One Too!! Describe how I would collect a random sample of 15 students from this class of 50. SRS?, Stratified? Cluster?

11. Talking about the “People” Be careful when making inferences from a sample to the population Biggest Problem/Misuse In Stats OVERGENERALIZATION Collecting Data from “relatively” small sample sizes and making inferences about a population The Bigger Your Sample – The More Accurate Your Inferences. Get a BIG Sample!! At least 15; try for 40 or more if possible.