Sampling Sections 1.2 & 1.3

Objectives Distinguish between an observational study and an experiment Learn and be able to obtain and distinguish between the following types of samples: simple random sample stratified sample systematic sample cluster sample

Terms – Ways to Collect Data Census – data collected from an entire population Observational study – Measures the characteristics of a population by studying individuals in a sample, but does not attempt to manipulate or influence the variable(s) of interest Experiment – Applies a treatment to subject (individuals) and attempts to isolate the effects of the treatment on a response variable. Existing Sources – Data already in existence.

Observational Study vs. Experiment What can we learn from observational studies? Can determine WHETHER there is a relationship between two variables. What can we learn from experiments? The CAUSE of the relationship between the two variables. Give examples of why we might want to use an observational study instead of an experiment. Give examples of why an experiment might be preferable.

Ex Post Facto Research “Ex post facto” – Latin for “after the fact” Refers to analysis that takes place after data has been collected for another purpose/study. Commonly used when researchers cannot control variables (economists, some dangerous medical experiments)

Sampling Goal – sample characteristics mimic the population characteristics for the variable in question. Why – so analysis of the sample can be extrapolated to the entire population. Four Sampling Techniques: Simple Random Stratified Systematic Cluster

Simple Random Sampling Simple Random Sampling – occurs when every possible sample of size “n” has an equally likely chance of occurring. Sample without replacement, so we don’t select the same event twice. In-Class Activity: Choose 5 students to represent a population. Form al possible samples of size n=2. How many are there? Select numbers from hat. Start over and select again. Are the students in the two different samples the same? Did they have the same chance of being selected? TI-84 Random Number function intro…

Stratified Sample Obtained by separating the population into non- overlapping groups (strata) and then obtaining a random sample from each group (stratum). Groups should consist of similar members of the population Examples of different strata:

Systematic Sample Systematic Sample: selecting every kth individual from the population. Example: Everyone line up at the front of the class… How many are there? How big do we want our sample to be? Divide population by sample and find “k” Count off… Step forward…

Cluster Sample Cluster sample – obtained by selecting all individuals within a randomly selected group. Example: factories randomly test carton(s) of “widgets” for quality.

What Kind of Sampling Every 20 th aircraft must be reviewed for RVSM compliance Grouping people by decades they were born in, find random samples to test for genetic mutations. A polling group randomly selects 20 families and calls them to ask them questions about upcoming elections. All the people who went to the dentist last Wednesday will be called for a survey.

Assignment Discuss: Pg 19: 1-3 and Pg 30, 1, 3-8 Due next class: Pg 19: 9-25 odd Pg 30: 9-17, 23-30