Presentation on theme: "Section 5.1. Observational Study vs. Experiment In an observational study, we observe individuals and measure variables of interest but do not attempt."— Presentation transcript:
Observational Study vs. Experiment In an observational study, we observe individuals and measure variables of interest but do not attempt to influence the responses. In an experiment, we deliberately impose some treatment on (that is, do something to) individuals in order to observe their responses.
Variables A response variable measures an outcome of a study. An explanatory variable helps explain or influences changes in a response variable.
Population and Sample The population in a statistical study is the entire group of individuals about which we want information. A sample is a part of the population that we actually examine in order to gather information.
Sampling A census attempts to include everyone in the population. Unlike a census, sampling involves studying a part in order to gain information about the whole. Sampling techniques include: voluntary response, convenience, simple random, stratified, systematic, and cluster. The sampling method is biased if it systematically favors certain outcomes.
The Idea of a Sample Survey Conclusions about a whole population are often drawn on the basis of a sample. Choosing a representative sample is not easy. Careful planning must take place. What population do we want to describe? What do we want to measure? Example: Current Population Survey (CPS) ○ Contact 60,000 household each month. ○ Produces the monthly unemployment. Other Examples?
Sampling Poorly Convenience sampling Choosing individuals who are easiest to reach. Where’s the Bias? Voluntary response Consists of people who choose themselves by responding to a general appeal Where’s the Bias?
Sampling Well Simple Random Sample (SRS) A SRS 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 to be the sample actually selected. NOTE: In this instance “random” does not mean haphazard as in “OMG that’s so random.” In statistics, random means “due to chance.”
Other Types of Sampling Stratified Random Sample Divide the population into similar groups (strata). Then choose a separate SRS in each stratum. Cluster Sample Divide the population into groups, or clusters. The clusters are randomly selected, then ALL individuals in the chosen clusters are in the sample. Systematic Sample Begin by selecting an element from the population at random and then every k th element is selected, where k, is the sampling interval.
Sampling Errors Undercoverage Occurs when some groups in the population are left out of the process of choosing the sample. Nonresponse Occurs when an individual chosen for the sample can’t be contacted or does not cooperate.
Nonsampling Errors Response Bias Giving incorrect responses Wording of Questions Confusing, leading, or order of questions can influence the outcome of a survey ○ Example: “How happy are you with your life in general? “How many dates did you have last month?”