1. AP Review #4: Sampling & Experimental Design

2. Sampling Techniques Simple Random Sample – Each combination of individuals has an equal chance of being selected (ex. number students 1-36 and use a random number generator to select 10) Stratified Sample – Population is divided into homogeneous groups by a characteristic (ex. grade level, zip code, etc.), then a random sample is chosen from each group Cluster Sample – Population is divided into naturally occurring, heterogeneous groups; a few groups are selected at random, and a census is conducted within each group Systematic Random Sample – Choose a random starting place from the first k individuals on a list, then choose every kth individual (ex. every 10 th person)

3. Sampling Considerations Good sampling techniques involve some element of random selection Sampling Frame = the list of individuals from which the sample will be chosen (we want this to be a list of the whole population, but this is not always practical) Representative samples look similar to the population Bias = a problem which makes the sample systematically off in one direction or another (non- representative)

4. Types of Bias in Sampling Response bias (ex. wording effect, interviewer effect) – Something about the question or delivery of the question leads people to answer a certain way Nonresponse bias – Individuals selected for the sample choose not to participate Selection bias – Poor method of sample selection (ex. convenience sampling) leads to a non-representative sample Voluntary response bias – Individuals decide to be part of the sample (write-in, call-in or internet polls); only those who feel strongly will respond Undercoverage – A certain portion of the population is left out when conducting the sample (ex. Those without telephones)

5. Experimental Design A study is an experiment (instead of an observational study) if a treatment is imposed Principles of Experimental Design 1)Control Comparison Blocking Placebos 2)Randomization Random assignment of subjects to treatment groups 3)Replication Many subjects, repeated experiments

6. Design Diagrams When you use these diagrams, you should also describe how you would do the randomization Label the fish from 1-20, then use a random number generator to select 10 fish for Group 1. The remaining 10 go in group 2.

7. Design Elements Experimental units (human = subjects) Factor (explanatory variable – ex. fish food) Levels (values of the factor – ex. old food & new food) Treatment (what you actually give the experimental units – combination of levels for all factors) Response variable – what you measure at the end

8. Blocking First divide the subjects into homogeneous blocks by a common characteristic, then carry out the randomized experiment separately within each block Blocking reduces the effect of confounding variables – only use it if there is variable that might make a difference in the response variable Paired design (matched pairs) = blocks of size 2