1. 1. What is one method of data collection? 2. What is a truly random way to survey/sample people?

3.  Observational study: We observe individuals and measure variables of interest but do not attempt to influence responses.  Experiment: We deliberately impose some treatment on individuals in order to observe their responses.  Pros vs. Cons of each? (control etc…experiment better)

4.  Pop: the entire group of individuals that we want information about  Sample: a part of the population that we actually examine in order to gather info  Sampling vs. Census: Sampling studies a part in order to gain info about the whole, census attempts to contact every individual in the pop

5.  Voluntary response: People choose themselves by responding  Convenience sampling: Choosing individuals who are easiest to reach  Bias: The sampling method is biased if it systematically favors certain outcomes

6.  The simplest way to use chance to select a sample is to place names in a hat (the population) and draw out a handful (the sample).  SRS: every individual has = chance of getting picked, every sample of the size you are drawing has = chance of getting picked

7.  Math, prb, randint(lowest #, highest #, # of people you want in your sample)  You can store your random numbers in a list:  Randint(1,150,25) sto-> L1

8.  Probability sample: samples chosen by chance  Stratified random sample: divide population into groups (aka strata) that are similar in some way, then choose a separate SRS in each stratum, then combine these SRS’s to form the full sample  Cluster sampling: divide population into groups (aka clusters). Some of these clusters are randomly selected. Then all individuals in chosen clusters are selected to be in the sample  Multistage samples

9.  Undercoverage: occurs when some groups in the population are left out in the process of choosing the sample (hard to get an accurate and complete list of the population. Most samples suffer from some degree of this)  Nonresponse: occurs when an individual chosen for the sample can’t be contacted or does not cooperate.

10.  The behavior of the respondent or interviewer can cause response bias in sample results  Wording of questions can influence answers  We can improve our results by knowing that larger random samples give more accurate results than smaller samples

11.  The individuals on which the experiment is done are the experimental units.  If units are humans, they are called subjects.  The experimental condition applied to the units (aka the thing we ‘do’ to the people participating) is called a treatment.  Goal of research is to establish a causal link between a particular treatment and a response.

12.  Factors: number of variables interested in (example: Study differences of gender and alcohol preference. 2 factors: Gender, alcohol preference)  Levels: number of ‘categories’ for each: (gender has 2 levels…M/F, Alcohol lets say has 3 levels…hard liquor/beer/wine)  This is an example of a 2x3 study

13.  We use lab experiments often to protect us from lurking variables which may happen when conducting experiments ‘in the field’

14.  We hope to see big differences (differences so large they are not likely just due to chance or individual differences).  If we do have an observed effect so large that it would rarely occur by chance, we call our result Statistically Significant

15.  Double-blind: neither subject nor experimenter knows which treatment is assigned  Lack of realism: subjects or treatments of an experiment may not realistically duplicate the conditions we really want to study.