1. Section 1 Part 1

2. Samples vs Population  Benefits of getting data from the entire population….  You can draw a conclusion about the entire population….more accurate  Benefits of sampling…  Easier to collect the data  Cost effective

3. Good Samples  A sample chosen by chance avoid bias by giving all individuals an equal chance to be chosen.

4. Simple Random Sample (SRS)  A simple random sample of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance of being selected.

5. Examples of SRS’  Choosing a name out of a hat or cup  Using a computer to randomly select the individuals  Use a random digit table to select the individuals.

6. Why is it so important to choose a sample Randomly?  ___________________________

7. How do we randomly choose samples in nature?  ________________________________

8. Fish in the lake Activity

9. Bad Samples  What make a sample bad?  Not representative of the population  Biased towards one portion of the population  Too small  Loaded questions  Inappropriate methods of collecting data

10. Example LOADED Questions  “Should the President have the line item veto to eliminate waste?” (97% said yes: )  “Should the President have the line item veto?” (57% said yes: )

11. Bias  The design of a statistical survey is biased if it systematically favors certain outcomes. BIASED SAMPLING METHODS  A Voluntary Response Sample chooses itself by responding to a general appeal. Write-in or call-in opinion polls are examples.  Ex; Sales feedback at car dealerships  Selection of whichever individuals are easiest to reach is called Convenience Sampling.  Survey’s in malls

12. Example Bad Sampling Methods – Create BIAS  5-Star Ford house in DFW asks their customers before they leave the dealership how they would rate their buying experience. If they would give a rating of 5, then they will be placed on the call list for customer service ratings.

13. Homework