1. Section 2.2: Sampling

2. Census – An entire population Reasons to use a census
Most common reason is limited resources
Restrictions on time and money

3. Types of Bias
Selection Bias – Tendency for samples to differ from the corresponding population as a result of systematic exclusion of some part of the population
Measurement or Response Bias – Tendency for samples to differ from the corresponding population because the method of observation tends to produce values that differ from the true value
Nonresponse Bias – Tendency for samples to differ from the corresponding population because data are not obtained from all individuals selected for inclusion in the sample

4. Simple Random Sample – A sample that is selected from a population in a way that ensures that every different possible sample of the desired size has the same chance of being selected.

5. Selecting a Simple Random Sample
Sampling Frame – A list of the objects or individuals in the population
Sampling with replacement – After each successive item is selected for the sample, the item is “replaced” back into the population and may therefore be selected again at a later stage.
Sampling without replacement – After being included in the sample, an individual or object would not be considered for further selection

6. Other Sampling Methods
Stratified Sampling – When the entire population can be divided into a set of nonoverlapping subgroups called strata
Cluster Sampling – Involves dividing the population of interest into nonoverlapping subgroups called clusters
Systematic Sampling – A procedure that can be used when it is possible to view the population of interest as consisting of a list or some other sequential arrangement.

7. Section 2.3: Statistical Studies: Observational and Experimental

8. Observational Study
The investigator observes characteristics of a subset of the members of one or more existing populations.
Goal of observational studies is to draw conclusions about the corresponding population or about differences between two or more populations.

9. Experiments
The investigator observes how a response variable behaves when the researcher manipulates one or more factors.
Factors – Variables that are manipulated in experiments
Goal of Experiments is to determine the effect of the manipulated factors on the response variable

10. Biggest difference between observational studies and experiments:
Experiments provide evidence for a cause-and-effect relationship
Observational Studies are impossible to draw cause-and-effect conclusions

11. Confounding Variables – Is related to both group membership and the response variable of interest in a research study.

12. Activity: Designing a Sampling Plan