1. Designing Studies In order to produce data that will truly answer the questions about a large group, the way a study is designed is important. 1)Decide on the population of interest. Population: an entire group of individuals from/about which information is wanting to be collected Sample: the part of the population that actually participates in the study or experiment A study of the population is called a census. A study of a sample is called a sampling.

2. Observational Study: Observe individuals and measure variables of interest without influencing the response Experiment: Deliberately apply some treatment to the individuals in order to observe the response vs. 2) Determine what type of study you are going to perform.

3. 3)Determine the sampling method (how you choose the actual sample to participate in your study) Sampling methods are important to ensure the conclusions can be extended to the entire population. Voluntary Response Sample Individuals choose to participate in the study as a response to a general appeal (Ex: call in polling) Convenience Sample Individuals chosen to participate in the study are easy to access (Ex: interviews at a mall) Both of these methods could create biased results (ie: design systematically favors certain outcomes) This is BAD !

4. The best way to choose the sample for a study or experiment is by a Simple Random Sample (SRS). This means a set of individuals are chosen in a way so that every individuals (or set of individuals) in the population had an equal chance to be selected. Without technology you can do this is by a Random Digits Table 1) Assign a number to each individual in the population (2 – 3 digits) 2) Starting at a particular row of the table, each 2 – 3 digits in the series will represent a member of the population (ignore repeated values) 3) Stop once you have reached the desired number of members in your sample.

5. Using 2 digit #’s for the members of this class (01 – 20): 62, 56, 87, 02, 06, 40, 32, 50, 36, 99, 71, 08, 02, 25, 53, 11, 48, 61, 17, 76

6. Other Types of Random Samples Stratified Random Sample: - population is divided into groups of individuals that have an important similarity (called strata) - an individual is chosen from each strata to create the sample Cluster Sample: - population is divided into groups or clusters - some of the clusters are randomly selected to create the sample

7. If it is not stated that a simple random sample (SRS) has been used in choosing the sample, then you must analyze the description of the study or experiment carefully to ensure that a random sampling method has been used. Warnings!!!! When dealing with humans, identifying a entire population can be difficult. Because of this a degree of undercoverage can occur. (some groups in the population will be left out)

8. When a member of the sample cannot be contacted or is uncooperative, this is called non-response. This along with response bias (lying) can cause issues with the study’s results. Wording of the question can also cause bias. Avoiding confusing or leading questions will help reduce this effect. When extending results to a population, larger samples will generally give more accurate results than smaller samples.